def pad_im(fname_im, nx_full, ny_full, nz_full, xi, xf, yi, yf, zi, zf):
fname_im_pad = sct.add_suffix(fname_im, "_pad")
pad_xi = str(xi)
pad_xf = str(nx_full - (xf + 1))
pad_yi = str(yi)
pad_yf = str(ny_full - (yf + 1))
pad_zi = str(zi)
pad_zf = str(nz_full - (zf + 1))
pad = ",".join([pad_xi, pad_xf, pad_yi, pad_yf, pad_zi, pad_zf])
if len(Image(fname_im).data.shape) == 5:
status, output = sct.run("sct_image -i " + fname_im + " -mcs")
s = "Created file(s):\n-->"
output_fnames = output[output.find(s) + len(s) :].split("\n")[0].split("'")
fname_comp_list = [output_fnames[i] for i in range(1, len(output_fnames), 2)]
fname_comp_pad_list = []
for fname_comp in fname_comp_list:
fname_comp_pad = sct.add_suffix(fname_comp, "_pad")
sct.run("sct_image -i " + fname_comp + " -pad-asym " + pad + " -o " + fname_comp_pad)
fname_comp_pad_list.append(fname_comp_pad)
components = ",".join(fname_comp_pad_list)
sct.run("sct_image -i " + components + " -omc -o " + fname_im_pad)
sct.check_file_exist(fname_im_pad, verbose=1)
else:
sct.run("sct_image -i " + fname_im + " -pad-asym " + pad + " -o " + fname_im_pad)
return fname_im_pad
def pad_im(fname_im, nx_full, ny_full, nz_full, xi, xf, yi, yf, zi, zf):
fname_im_pad = sct.add_suffix(fname_im, '_pad')
pad_xi = str(xi)
pad_xf = str(nx_full - (xf + 1))
pad_yi = str(yi)
pad_yf = str(ny_full - (yf + 1))
pad_zi = str(zi)
pad_zf = str(nz_full - (zf + 1))
pad = ','.join([pad_xi, pad_xf, pad_yi, pad_yf, pad_zi, pad_zf])
if len(Image(fname_im).data.shape) == 5:
status, output = sct.run('sct_image -i ' + fname_im + ' -mcs')
s = 'Created file(s):\n-->'
output_fnames = output[output.find(s) +
len(s):].split('\n')[0].split("'")
fname_comp_list = [
output_fnames[i] for i in range(1, len(output_fnames), 2)
]
fname_comp_pad_list = []
for fname_comp in fname_comp_list:
fname_comp_pad = sct.add_suffix(fname_comp, '_pad')
sct.run('sct_image -i ' + fname_comp + ' -pad-asym ' + pad +
' -o ' + fname_comp_pad)
fname_comp_pad_list.append(fname_comp_pad)
components = ','.join(fname_comp_pad_list)
sct.run('sct_image -i ' + components + ' -omc -o ' + fname_im_pad)
sct.check_file_exist(fname_im_pad, verbose=1)
else:
sct.run('sct_image -i ' + fname_im + ' -pad-asym ' + pad + ' -o ' +
fname_im_pad)
return fname_im_pad
def main():
# Initialization
path_script = os.path.dirname(__file__)
fsloutput = "export FSLOUTPUTTYPE=NIFTI; " # for faster processing, all outputs are in NIFTI
# THIS DOES NOT WORK IN MY LAPTOP: path_sct = os.environ['SCT_DIR'] # path to spinal cord toolbox
# path_sct = path_script[:-8] # TODO: make it cleaner!
status, path_sct = commands.getstatusoutput("echo $SCT_DIR")
fname_segmentation = ""
name_process = ""
processes = ["extract_centerline", "compute_CSA"]
verbose = param.verbose
start_time = time.time()
remove_temp_files = param.remove_temp_files
# Parameters for debug mode
if param.debug:
fname_segmentation = path_sct + "/testing/data/errsm_23/t2/t2_manual_segmentation.nii.gz"
verbose = 1
remove_temp_files = 0
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], "hi:p:v:")
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == "-h":
usage()
elif opt in ("-i"):
fname_segmentation = arg
elif opt in ("-p"):
name_process = arg
elif opt in ("-v"):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_segmentation == "" or name_process == "":
usage()
# display usage if the requested process is not available
if name_process not in processes:
usage()
# check existence of input files
sct.check_file_exist(fname_segmentation)
# print arguments
print "\nCheck parameters:"
print ".. segmentation file: " + fname_segmentation
if name_process == "extract_centerline":
extract_centerline(fname_segmentation)
if name_process == "compute_CSA":
compute_CSA(fname_segmentation)
# display elapsed time
elapsed_time = time.time() - start_time
print "\nFinished! Elapsed time: " + str(int(round(elapsed_time))) + "s"
def main():
# Initialization
fname_input = ''
fname_segmentation = ''
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput(
'echo $SCT_TESTING_DATA_DIR')
fname_input = ''
fname_segmentation = path_sct_data + '/t2/t2_seg.nii.gz'
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:t:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_input = arg
elif opt in ('-t'):
fname_segmentation = arg
# display usage if a mandatory argument is not provided
if fname_segmentation == '' or fname_input == '':
usage()
# check existence of input files
sct.check_file_exist(fname_input)
sct.check_file_exist(fname_segmentation)
# read nifti input file
img = nibabel.load(fname_input)
# 3d array for each x y z voxel values for the input nifti image
data = img.get_data()
# read nifti input file
img_seg = nibabel.load(fname_segmentation)
# 3d array for each x y z voxel values for the input nifti image
data_seg = img_seg.get_data()
X, Y, Z = (data > 0).nonzero()
status = 0
for i in range(0, len(X)):
if data_seg[X[i], Y[i], Z[i]] == 0:
status = 1
break
if status is not 0:
sct.printv('ERROR: detected point is not in segmentation', 1,
'warning')
else:
sct.printv('OK: detected point is in segmentation')
sys.exit(status)
def main():
# Initialization
fname_input = ''
fname_segmentation = ''
if param.debug:
sct.printv( '\n*** WARNING: DEBUG MODE ON ***\n')
path_sct_data = os.path.join(sct.__data_dir__, "sct_testing_data")
fname_input = ''
fname_segmentation = os.path.join(path_sct_data, 't2', 't2_seg.nii.gz')
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:t:')
except getopt.GetoptError as err:
logger.error(str(err))
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_input = arg
elif opt in ('-t'):
fname_segmentation = arg
# display usage if a mandatory argument is not provided
if fname_segmentation == '' or fname_input == '':
usage()
# check existence of input files
sct.check_file_exist(fname_input)
sct.check_file_exist(fname_segmentation)
# read nifti input file
img = nibabel.load(fname_input)
# 3d array for each x y z voxel values for the input nifti image
data = img.get_data()
# read nifti input file
img_seg = nibabel.load(fname_segmentation)
# 3d array for each x y z voxel values for the input nifti image
data_seg = img_seg.get_data()
X, Y, Z = (data > 0).nonzero()
status = 0
for i in range(0, len(X)):
if data_seg[X[i], Y[i], Z[i]] == 0:
status = 1
break;
if status is not 0:
sct.printv('ERROR: detected point is not in segmentation', 1, 'warning')
else:
sct.printv('OK: detected point is in segmentation')
sys.exit(status)
def main():
#Initialization
fname = ''
verbose = param.verbose
start = ''
end = ''
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:e:s:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ('-s'):
start = int(arg)
elif opt in ('-e'):
end = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname == '' :
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Verbose ........................... '+str(verbose)
file = nibabel.load(fname)
data = file.get_data()
hdr = file.get_header()
for i in range(start,end+1):
data[:,:,i] = 0
print '\nSave volume ...'
hdr.set_data_dtype('float32') # set imagetype to uint8
# save volume
#data = data.astype(float32, copy =False)
img = nibabel.Nifti1Image(data, None, hdr)
file_name = 'centerline_erased.nii.gz'
nibabel.save(img,file_name)
def main():
# Initialization
fname_input = ''
fname_segmentation = ''
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_input = ''
fname_segmentation = path_sct_data+'/t2/t2_seg.nii.gz'
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:t:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_input = arg
elif opt in ('-t'):
fname_segmentation = arg
# display usage if a mandatory argument is not provided
if fname_segmentation == '' or fname_input == '':
usage()
# check existence of input files
sct.check_file_exist(fname_input)
sct.check_file_exist(fname_segmentation)
# read nifti input file
img = nibabel.load(fname_input)
# 3d array for each x y z voxel values for the input nifti image
data = img.get_data()
# read nifti input file
img_seg = nibabel.load(fname_segmentation)
# 3d array for each x y z voxel values for the input nifti image
data_seg = img_seg.get_data()
X, Y, Z = (data>0).nonzero()
status = 0
for i in range(0,len(X)):
if data_seg(X[i],Y[i],Z[i]) == 0:
status = 1
break;
if status is not 0:
sct.printv('ERROR: detected point is not in segmentation',1,'error')
else:
sct.printv('OK: detected point is in segmentation')
sys.exit(0)
def main():
#Initialization
fname = ''
verbose = param.verbose
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname == '' :
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Verbose ........................... '+str(verbose)
file = nibabel.load(fname)
data = file.get_data()
hdr = file.get_header()
X,Y,Z = (data>0).nonzero()
x_max,y_max = (data[:,:,max(Z)]).nonzero()
x_max = x_max[0]
y_max = y_max[0]
z_max = max(Z)
x_min,y_min = (data[:,:,min(Z)]).nonzero()
x_min = x_min[0]
y_min = y_min[0]
z_min = min(Z)
del data
print 'Coords extrema : min [ ' + str(x_min) + ' ,' + str(y_min) + ' ,' + str(z_min) +' ] max [ ' + str(x_max) + ' ,' + str(y_max) + ' ,' + str(z_max) + ' ]'
return z_min,z_max
def segmentation(fname_input, output_dir, image_type):
# parameters
path_in, file_in, ext_in = sct.extract_fname(fname_input)
# define command
cmd = 'sct_propseg_test -i ' + fname_input \
+ ' -o ' + output_dir \
+ ' -t ' + image_type \
+ ' -detect-nii' \
status, output = sct.run(cmd)
# check if spinal cord is correctly detected
# sct_propseg return one point
# check existence of input files
segmentation_filename = path_in + file_in + '_seg' + ext_in
manual_segmentation_filename = path_in + 'manual_' + file_in + ext_in
detection_filename = path_in + file_in + '_detection' + ext_in
sct.check_file_exist(detection_filename)
sct.check_file_exist(segmentation_filename)
# read nifti input file
img = nibabel.load(detection_filename)
# 3d array for each x y z voxel values for the input nifti image
data = img.get_data()
# read nifti input file
img_seg = nibabel.load(manual_segmentation_filename)
# 3d array for each x y z voxel values for the input nifti image
data_seg = img_seg.get_data()
X, Y, Z = (data > 0).nonzero()
status = 0
for i in range(0, len(X)):
if data_seg[X[i], Y[i], Z[i]] == 0:
status = 1
break
if status is not 0:
sct.printv('ERROR: detected point is not in segmentation', 1,
'warning')
else:
sct.printv('OK: detected point is in segmentation')
cmd_validation = 'sct_dice_coefficient ' + segmentation_filename \
+ ' ' + manual_segmentation_filename \
+ ' -bzmax'
status_validation, output = sct.run(cmd_validation)
print output
return status
def segmentation(fname_input, output_dir, image_type):
# parameters
path_in, file_in, ext_in = sct.extract_fname(fname_input)
# define command
cmd = 'sct_propseg_test -i ' + fname_input \
+ ' -o ' + output_dir \
+ ' -t ' + image_type \
+ ' -detect-nii' \
status, output = sct.run(cmd)
# check if spinal cord is correctly detected
# sct_propseg return one point
# check existence of input files
segmentation_filename = path_in + file_in + '_seg' + ext_in
manual_segmentation_filename = path_in + 'manual_' + file_in + ext_in
detection_filename = path_in + file_in + '_detection' + ext_in
sct.check_file_exist(detection_filename)
sct.check_file_exist(segmentation_filename)
# read nifti input file
img = nibabel.load(detection_filename)
# 3d array for each x y z voxel values for the input nifti image
data = img.get_data()
# read nifti input file
img_seg = nibabel.load(manual_segmentation_filename)
# 3d array for each x y z voxel values for the input nifti image
data_seg = img_seg.get_data()
X, Y, Z = (data>0).nonzero()
status = 0
for i in range(0,len(X)):
if data_seg[X[i],Y[i],Z[i]] == 0:
status = 1
break;
if status is not 0:
sct.printv('ERROR: detected point is not in segmentation',1,'warning')
else:
sct.printv('OK: detected point is in segmentation')
cmd_validation = 'sct_dice_coefficient ' + segmentation_filename \
+ ' ' + manual_segmentation_filename \
+ ' -bzmax'
status_validation, output = sct.run(cmd_validation)
print output
return status
def read_label_file(path_info_label):
# file name of info_label.txt
fname_label = path_info_label + param.file_info_label
# Check info_label.txt existence
sct.check_file_exist(fname_label)
# Read file
f = open(fname_label)
# Extract all lines in file.txt
lines = [lines for lines in f.readlines() if lines.strip()]
# separate header from (every line starting with "#")
lines = [lines[i] for i in range(0, len(lines)) if lines[i][0] != '#']
# read each line
label_id = []
label_name = []
label_file = []
for i in range(0, len(lines) - 1):
line = lines[i].split(',')
label_id.append(int(line[0]))
label_name.append(line[1])
label_file.append(line[2][:-1].strip())
# An error could occur at the last line (deletion of the last character of the .txt file), the 5 following code
# lines enable to avoid this error:
line = lines[-1].split(',')
label_id.append(int(line[0]))
label_name.append(line[1])
line[2] = line[2] + ' '
label_file.append(line[2].strip())
# check if all files listed are present in folder. If not, WARNING.
sct.printv('\nCheck existence of all files listed in ' +
param.file_info_label + ' ...')
for fname in label_file:
if os.path.isfile(path_info_label + fname) or os.path.isfile(path_info_label + fname + '.nii') or \
os.path.isfile(path_info_label + fname + '.nii.gz'):
sct.printv(' OK: ' + path_info_label + fname)
else:
sct.printv(' WARNING: ' + path_info_label + fname +
' does not exist but is listed in ' +
param.file_info_label + '.\n')
# Close file.txt
f.close()
return [label_id, label_name, label_file]
def read_label_file(path_info_label):
# file name of info_label.txt
fname_label = path_info_label+param.file_info_label
# Check info_label.txt existence
sct.check_file_exist(fname_label)
# Read file
f = open(fname_label)
# Extract all lines in file.txt
lines = [lines for lines in f.readlines() if lines.strip()]
# separate header from (every line starting with "#")
lines = [lines[i] for i in range(0, len(lines)) if lines[i][0] != '#']
# read each line
label_id = []
label_name = []
label_file = []
for i in range(0, len(lines)-1):
line = lines[i].split(',')
label_id.append(int(line[0]))
label_name.append(line[1])
label_file.append(line[2][:-1].strip())
# An error could occur at the last line (deletion of the last character of the .txt file), the 5 following code
# lines enable to avoid this error:
line = lines[-1].split(',')
label_id.append(int(line[0]))
label_name.append(line[1])
line[2]=line[2]+' '
label_file.append(line[2].strip())
# check if all files listed are present in folder. If not, WARNING.
print '\nCheck existence of all files listed in '+param.file_info_label+' ...'
for fname in label_file:
if os.path.isfile(path_info_label+fname) or os.path.isfile(path_info_label+fname + '.nii') or \
os.path.isfile(path_info_label+fname + '.nii.gz'):
print(' OK: '+path_info_label+fname)
pass
else:
print(' WARNING: ' + path_info_label+fname + ' does not exist but is listed in '
+param.file_info_label+'.\n')
# Close file.txt
f.close()
return [label_id, label_name, label_file]
def loadFromPath(self, path, verbose):
"""
This function load an image from an absolute path using nibabel library
:param path: path of the file from which the image will be loaded
:return:
"""
from nibabel import load, spatialimages
from sct_utils import check_file_exist, printv, extract_fname
from sct_orientation import get_orientation
check_file_exist(path, verbose=verbose)
try:
im_file = load(path)
except spatialimages.ImageFileError:
printv('Error: make sure ' + path + ' is an image.', 1, 'error')
self.orientation = get_orientation(path)
self.data = im_file.get_data()
self.hdr = im_file.get_header()
self.absolutepath = path
self.path, self.file_name, self.ext = extract_fname(path)
def check_do_files_exist(fname_template, fname_template_vertebral_labeling,
fname_template_seg, verbose):
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
def loadFromPath(self, path, verbose):
"""
This function load an image from an absolute path using nibabel library
:param path: path of the file from which the image will be loaded
:return:
"""
from nibabel import load, spatialimages
from sct_utils import check_file_exist, printv, extract_fname, get_dimension
from sct_orientation import get_orientation
check_file_exist(path, verbose=verbose)
try:
im_file = load(path)
except spatialimages.ImageFileError:
printv('Error: make sure ' + path + ' is an image.', 1, 'error')
self.orientation = get_orientation(path)
self.data = im_file.get_data()
self.hdr = im_file.get_header()
self.absolutepath = path
self.path, self.file_name, self.ext = extract_fname(path)
nx, ny, nz, nt, px, py, pz, pt = get_dimension(path)
self.dim = [nx, ny, nz]
def get_or_set_orientation():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# display usage if a mandatory argument is not provided
if param.fname_data == '':
sct.printv('ERROR: All mandatory arguments are not provided. See usage.', 1, 'error')
# check existence of input files
sct.printv('\ncheck existence of input files...', param.verbose)
sct.check_file_exist(param.fname_data, param.verbose)
# find what to do
if param.orientation == '':
todo = 'get_orientation'
else:
todo = 'set_orientation'
# check if orientation is correct
if check_orientation_input():
sct.printv('\nERROR in '+os.path.basename(__file__)+': orientation is not recognized. Use one of the following orientation: '+param.list_of_correct_orientation+'\n', 1, 'error')
sys.exit(2)
# display input parameters
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' data ..................'+param.fname_data, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
if param.fname_out == '':
# path_out, file_out, ext_out = '', file_data+'_'+param.orientation, ext_data
fname_out = path_data+file_data+'_'+param.orientation+ext_data
else:
fname_out = param.fname_out
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, param.verbose)
# Copying input data to tmp folder and convert to nii
# NB: cannot use c3d here because c3d cannot convert 4D data.
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
sct.run('fslchfiletype NIFTI data', param.verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('data.nii')
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
# if 4d, loop across the data
if nt == 1:
if todo == 'set_orientation':
# set orientation
sct.printv('\nChange orientation...', param.verbose)
set_orientation('data.nii', param.orientation, 'data_orient.nii')
elif todo == 'get_orientation':
# get orientation
sct.printv('\nGet orientation...', param.verbose)
sct.printv(get_orientation('data.nii'), 1)
else:
# split along T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
sct.run(fsloutput+'fslsplit data data_T', param.verbose)
if todo == 'set_orientation':
# set orientation
sct.printv('\nChange orientation...', param.verbose)
for it in range(nt):
file_data_split = 'data_T'+str(it).zfill(4)+'.nii'
file_data_split_orient = 'data_orient_T'+str(it).zfill(4)+'.nii'
set_orientation(file_data_split, param.orientation, file_data_split_orient)
# Merge files back
sct.printv('\nMerge file back...', param.verbose)
cmd = fsloutput+'fslmerge -t data_orient'
for it in range(nt):
file_data_split_orient = 'data_orient_T'+str(it).zfill(4)+'.nii'
cmd = cmd+' '+file_data_split_orient
sct.run(cmd, param.verbose)
elif todo == 'get_orientation':
sct.printv('\nGet orientation...', param.verbose)
sct.printv(get_orientation('data_T0000.nii'), 1)
# come back to parent folder
os.chdir('..')
# Generate output files
if todo == 'set_orientation':
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'data_orient.nii', fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose)
# to view results
if todo == 'set_orientation':
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+fname_out+' &', param.verbose, 'code')
print
def __init__(self, fname_src, fname_transfo, warp_atlas, warp_spinal_levels, folder_out, path_template, verbose):
# Initialization
self.fname_src = ''
self.fname_transfo = ''
self.folder_out = param.folder_out
self.path_template = param.path_template
self.folder_template = param.folder_template
self.folder_atlas = param.folder_atlas
self.folder_spinal_levels = param.folder_spinal_levels
self.warp_template = param.warp_template
self.warp_atlas = param.warp_atlas
self.warp_spinal_levels = param.warp_spinal_levels
self.verbose = param.verbose
start_time = time.time()
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_src = path_sct+'/testing/sct_testing_data/data/mt/mtr.nii.gz'
fname_transfo = path_sct+'/testing/sct_testing_data/data/mt/warp_template2mt.nii.gz'
warp_atlas = 1
warp_spinal_levels = 1
verbose = 1
else:
self.fname_src = fname_src
self.fname_transfo = fname_transfo
self.warp_atlas = warp_atlas
self.warp_spinal_levels = warp_spinal_levels
self.folder_out = folder_out
self.path_template = path_template
self.verbose = verbose
# Check file existence
sct.printv('\nCheck file existence...', self.verbose)
sct.check_file_exist(self.fname_src)
sct.check_file_exist(self.fname_transfo)
# add slash at the end of folder name (in case there is no slash)
self.path_template = sct.slash_at_the_end(self.path_template, 1)
self.folder_out = sct.slash_at_the_end(self.folder_out, 1)
self.folder_template = sct.slash_at_the_end(self.folder_template, 1)
self.folder_atlas = sct.slash_at_the_end(self.folder_atlas, 1)
self.folder_spinal_levels = sct.slash_at_the_end(self.folder_spinal_levels, 1)
# print arguments
print '\nCheck parameters:'
print ' Destination image ........ '+self.fname_src
print ' Warping field ............ '+self.fname_transfo
print ' Path template ............ '+self.path_template
print ' Output folder ............ '+self.folder_out+'\n'
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(self.fname_src)
# create output folder
if os.path.exists(self.folder_out):
sct.printv('WARNING: Output folder already exists. Deleting it...', self.verbose)
sct.run('rm -rf '+self.folder_out)
sct.run('mkdir '+self.folder_out)
# Warp template objects
if self.warp_template == 1:
sct.printv('\nWarp template objects...', self.verbose)
warp_label(self.path_template, self.folder_template, param.file_info_label, self.fname_src, self.fname_transfo, self.folder_out)
# Warp atlas
if self.warp_atlas == 1:
sct.printv('\nWarp atlas of white matter tracts...', self.verbose)
warp_label(self.path_template, self.folder_atlas, param.file_info_label, self.fname_src, self.fname_transfo, self.folder_out)
# Warp spinal levels
if self.warp_spinal_levels == 1:
sct.printv('\nWarp spinal levels...', self.verbose)
warp_label(self.path_template, self.folder_spinal_levels, param.file_info_label, self.fname_src, self.fname_transfo, self.folder_out)
# to view results
sct.printv('\nDone! To view results, type:', self.verbose)
sct.printv('fslview '+self.fname_src+' '+self.folder_out+self.folder_template+'MNI-Poly-AMU_T2.nii.gz -b 0,4000 '+self.folder_out+self.folder_template+'MNI-Poly-AMU_level.nii.gz -l MGH-Cortical -t 0.5 '+self.folder_out+self.folder_template+'MNI-Poly-AMU_GM.nii.gz -l Red-Yellow -b 0.5,1 '+self.folder_out+self.folder_template+'MNI-Poly-AMU_WM.nii.gz -l Blue-Lightblue -b 0.5,1 &\n', self.verbose, 'info')
def __init__(self, fname_src, fname_transfo, warp_atlas, warp_spinal_levels, folder_out, path_template, verbose, qc):
# Initialization
self.fname_src = fname_src
self.fname_transfo = fname_transfo
self.warp_atlas = warp_atlas
self.warp_spinal_levels = warp_spinal_levels
self.folder_out = folder_out
self.path_template = path_template
self.folder_template = param.folder_template
self.folder_atlas = param.folder_atlas
self.folder_spinal_levels = param.folder_spinal_levels
self.verbose = verbose
self.qc = qc
start_time = time.time()
# Check file existence
sct.printv('\nCheck file existence...', self.verbose)
sct.check_file_exist(self.fname_src)
sct.check_file_exist(self.fname_transfo)
# add slash at the end of folder name (in case there is no slash)
self.path_template = sct.slash_at_the_end(self.path_template, 1)
self.folder_out = sct.slash_at_the_end(self.folder_out, 1)
self.folder_template = sct.slash_at_the_end(self.folder_template, 1)
self.folder_atlas = sct.slash_at_the_end(self.folder_atlas, 1)
self.folder_spinal_levels = sct.slash_at_the_end(self.folder_spinal_levels, 1)
# print arguments
print '\nCheck parameters:'
print ' Destination image ........ '+self.fname_src
print ' Warping field ............ '+self.fname_transfo
print ' Path template ............ '+self.path_template
print ' Output folder ............ '+self.folder_out+'\n'
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(self.fname_src)
# create output folder
if os.path.exists(self.folder_out):
sct.printv('WARNING: Output folder already exists. Deleting it...', self.verbose, 'warning')
sct.run('rm -rf '+self.folder_out)
sct.run('mkdir '+self.folder_out)
# Warp template objects
sct.printv('\nWarp template objects...', self.verbose)
warp_label(self.path_template, self.folder_template, param.file_info_label, self.fname_src, self.fname_transfo, self.folder_out)
# Warp atlas
if self.warp_atlas == 1:
sct.printv('\nWarp atlas of white matter tracts...', self.verbose)
warp_label(self.path_template, self.folder_atlas, param.file_info_label, self.fname_src, self.fname_transfo, self.folder_out)
# Warp spinal levels
if self.warp_spinal_levels == 1:
sct.printv('\nWarp spinal levels...', self.verbose)
warp_label(self.path_template, self.folder_spinal_levels, param.file_info_label, self.fname_src, self.fname_transfo, self.folder_out)
# to view results
sct.printv('\nDone! To view results, type:', self.verbose)
sct.printv('fslview '+self.fname_src+' '+self.folder_out+self.folder_template+'MNI-Poly-AMU_T2.nii.gz -b 0,4000 '+self.folder_out+self.folder_template+'MNI-Poly-AMU_level.nii.gz -l MGH-Cortical -t 0.5 '+self.folder_out+self.folder_template+'MNI-Poly-AMU_GM.nii.gz -l Red-Yellow -b 0.5,1 '+self.folder_out+self.folder_template+'MNI-Poly-AMU_WM.nii.gz -l Blue-Lightblue -b 0.5,1 &\n', self.verbose, 'info')
if self.qc:
from msct_image import Image
# output QC image
im = Image(self.fname_src)
im_wm = Image(self.folder_out+self.folder_template+'MNI-Poly-AMU_WM.nii.gz')
im.save_quality_control(plane='axial', n_slices=4, seg=im_wm, thr=0.5, cmap_col='blue-cyan', path_output=self.folder_out)
def main():
# Initialization
fname_src = ''
fname_transfo = ''
folder_out = param.folder_out
path_template = param.path_template
folder_template = param.folder_template
folder_atlas = param.folder_atlas
folder_spinal_levels = param.folder_spinal_levels
file_info_label = param.file_info_label
warp_template = param.warp_template
warp_atlas = param.warp_atlas
warp_spinal_levels = param.warp_spinal_levels
verbose = param.verbose
start_time = time.time()
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_src = path_sct+'/testing/sct_testing_data/data/mt/mtr.nii.gz'
fname_transfo = path_sct+'/testing/sct_testing_data/data/mt/warp_template2mt.nii.gz'
warp_atlas = 1
warp_spinal_levels = 1
verbose = 1
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'ha:d:w:o:s:t:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-a"):
warp_atlas = int(arg)
elif opt in ("-d"):
fname_src = arg
elif opt in ("-o"):
folder_out = arg
elif opt in ("-t"):
path_template = arg
elif opt in ("-s"):
warp_spinal_levels = int(arg)
elif opt in ('-v'):
verbose = int(arg)
elif opt in ("-w"):
fname_transfo = arg
# display usage if a mandatory argument is not provided
if fname_src == '' or fname_transfo == '':
usage()
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_src)
sct.check_file_exist(fname_transfo)
# add slash at the end of folder name (in case there is no slash)
path_template = sct.slash_at_the_end(path_template, 1)
folder_out = sct.slash_at_the_end(folder_out, 1)
folder_template = sct.slash_at_the_end(folder_template, 1)
folder_atlas = sct.slash_at_the_end(folder_atlas, 1)
folder_spinal_levels = sct.slash_at_the_end(folder_spinal_levels, 1)
# print arguments
print '\nCheck parameters:'
print ' Destination image ........ '+fname_src
print ' Warping field ............ '+fname_transfo
print ' Path template ............ '+path_template
print ' Output folder ............ '+folder_out+'\n'
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
# create output folder
if os.path.exists(folder_out):
sct.printv('WARNING: Output folder already exists. Deleting it...', verbose)
sct.run('rm -rf '+folder_out)
sct.run('mkdir '+folder_out)
# Warp template objects
if warp_template == 1:
sct.printv('\nWarp template objects...', verbose)
warp_label(path_template, folder_template, param.file_info_label, fname_src, fname_transfo, folder_out)
# Warp atlas
if warp_atlas == 1:
sct.printv('\nWarp atlas of white matter tracts...', verbose)
warp_label(path_template, folder_atlas, param.file_info_label, fname_src, fname_transfo, folder_out)
# Warp spinal levels
if warp_spinal_levels == 1:
sct.printv('\nWarp spinal levels...', verbose)
warp_label(path_template, folder_spinal_levels, param.file_info_label, fname_src, fname_transfo, folder_out)
# to view results
sct.printv('\nDone! To view results, type:', verbose)
sct.printv('fslview '+fname_src+' '+folder_out+folder_template+'MNI-Poly-AMU_T2.nii.gz -b 0,4000 '+folder_out+folder_template+'MNI-Poly-AMU_level.nii.gz -l MGH-Cortical -t 0.5 '+folder_out+folder_template+'MNI-Poly-AMU_GM.nii.gz -l Red-Yellow -b 0.5,1 '+folder_out+folder_template+'MNI-Poly-AMU_WM.nii.gz -l Blue-Lightblue -b 0.5,1 &\n', verbose, 'info')
def main():
# get path of the toolbox
status, path_sct = getstatusoutput('echo $SCT_DIR')
#print path_sct
#Initialization
fname = ''
landmarks_native = ''
landmarks_template = path_sct + '/dev/template_creation/template_landmarks-mm.nii.gz'
reference = path_sct + '/dev/template_creation/template_shape.nii.gz'
verbose = param.verbose
interpolation_method = 'spline'
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:n:t:R:v:a:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-n"):
landmarks_native = arg
elif opt in ("-t"):
landmarks_template = arg
elif opt in ("-R"):
reference = arg
elif opt in ('-v'):
verbose = int(arg)
elif opt in ('-a'):
interpolation_method = str(arg)
# display usage if a mandatory argument is not provided
if fname == '' :
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
sct.check_file_exist(landmarks_native)
sct.check_file_exist(landmarks_template)
sct.check_file_exist(reference)
path_input, file_input, ext_input = sct.extract_fname(fname)
output_name = path_input + file_input + '_2temp' + ext_input
print output_name
transfo = 'native2temp.txt'
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Landmarks in native space ...................... '+landmarks_native
print' Landmarks in template space ...................... '+landmarks_template
print' Reference ...................... '+reference
print' Verbose ........................... '+str(verbose)
print '\nEstimate rigid transformation between paired landmarks...'
sct.run('ANTSUseLandmarkImagesToGetAffineTransform ' + landmarks_template + ' '+ landmarks_native + ' affine ' + transfo)
# Apply rigid transformation
print '\nApply affine transformation to native landmarks...'
sct.run('sct_apply_transfo -i ' + fname + ' -o ' + output_name + ' -d ' + reference + ' -w ' + transfo +' -x ' + interpolation_method)
# sct.run('WarpImageMultiTransform 3 ' + fname + ' ' + output_name + ' -R ' + reference + ' ' + transfo)
print '\nFile created : ' + output_name
def crop_with_gui(self):
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Initialization
fname_data = self.input_filename
suffix_out = '_crop'
remove_temp_files = self.rm_tmp_files
verbose = self.verbose
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' data ................... ' + fname_data)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
path_tmp = sct.tmp_create() + "/"
# copy files into tmp folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
Image('data.nii').change_orientation("RPI").save('data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title('Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.')
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n', 1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.absolutepath = 'data_rpi_crop.nii'
nii.save()
# come back
os.chdir(curdir)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "data_rpi_crop.nii"), os.path.join(path_out, file_out + ext_out))
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
sct.display_viewer_syntax(files=[os.path.join(path_out, file_out + ext_out)])
def main():
# Initialization
path_script = os.path.dirname(__file__)
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# THIS DOES NOT WORK IN MY LAPTOP: path_sct = os.environ['SCT_DIR'] # path to spinal cord toolbox
path_sct = path_script[:-8] # TODO: make it cleaner!
fname_data = ''
fname_bvecs = ''
verbose = param.verbose
start_time = time.time()
# Parameters for debug mode
if param.debug:
fname_data = os.path.expanduser("~")+'/code/spinalcordtoolbox_dev/testing/data/errsm_22/dmri/dmri.nii.gz'
fname_bvecs = os.path.expanduser("~")+'/code/spinalcordtoolbox_dev/testing/data/errsm_22/dmri/bvecs.txt'
verbose = 1
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'hb:i:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-b"):
fname_bvecs = arg
elif opt in ("-i"):
fname_data = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '' or fname_bvecs == '':
usage()
# check existence of input files
sct.check_file_exist(fname_data)
sct.check_file_exist(fname_bvecs)
# print arguments
print '\nCheck parameters:'
print '.. DWI data: '+fname_data
print '.. bvecs file: '+fname_bvecs
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.run('cp '+fname_data+' '+path_tmp)
sct.run('cp '+fname_bvecs+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_data)
print '.. '+str(nx)+' x '+str(ny)+' x '+str(nz)+' x '+str(nt)
# Open bvecs file
bvecs = []
with open(fname_bvecs) as f:
for line in f:
bvecs_new = map(float, line.split())
bvecs.append(bvecs_new)
# Check if bvecs file is nx3
if not len(bvecs[0][:]) == 3:
print 'WARNING: bvecs file is 3xn instead of nx3. Consider using sct_dmri_transpose_bvecs'
# transpose bvecs
bvecs = zip(*bvecs)
# Identify b=0 and DW images
print '\nIdentify b=0 and DW images...'
index_b0 = []
index_dwi = []
for it in xrange(0,nt):
if math.sqrt(math.fsum([i**2 for i in bvecs[it]])) < 0.01:
index_b0.append(it)
else:
index_dwi.append(it)
nb_b0 = len(index_b0)
nb_dwi = len(index_dwi)
print '.. Number of b=0: '+str(nb_b0)+' '+str(index_b0)
print '.. Number of DWI: '+str(nb_dwi)+' '+str(index_dwi)
#TODO: check if number of bvecs and nt match
# Split into T dimension
print '\nSplit along T dimension...'
sct.run(fsloutput+' fslsplit '+fname_data+' data_splitT')
# retrieve output names
status, output = sct.run('ls data_splitT*.*')
file_data_split = output.split()
# Remove .nii extension
file_data_split = [file_data_split[i].replace('.nii','') for i in xrange (0,len(file_data_split))]
# Merge b=0 images
print '\nMerge b=0...'
cmd = fsloutput+'fslmerge -t b0'
for it in xrange(0,nb_b0):
cmd += ' '+file_data_split[index_b0[it]]
sct.run(cmd)
# Merge DWI images
print '\nMerge DWI...'
cmd = fsloutput+'fslmerge -t dwi'
for it in xrange(0,nb_dwi):
cmd += ' '+file_data_split[index_dwi[it]]
sct.run(cmd)
# come back to parent folder
os.chdir('..')
# Generate output files
print('\nGenerate output files...')
sct.generate_output_file(path_tmp+'/b0.nii',path_data,'b0',ext_data)
sct.generate_output_file(path_tmp+'/dwi.nii',path_data,'dwi',ext_data)
# Remove temporary files
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s'
# to view results
print '\nTo view results, type:'
print 'fslview b0 dwi &\n'
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
# file_template_label = param.file_template_label
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# retrieve template file names
file_template_vertebral_labeling = get_file_label(
os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(
os.path.join(path_template, 'template'),
contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'),
'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(
path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template',
file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template',
'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(remove_temp_files), verbose)
# check if data, segmentation and landmarks are in the same space
# JULIEN 2017-04-25: removed because of issue #1168
# sct.printv('\nCheck if data, segmentation and landmarks are in the same space...')
# if not sct.check_if_same_space(fname_data, fname_seg):
# sct.printv('ERROR: Data image and segmentation are not in the same space. Please check space and orientation of your files', verbose, 'error')
# if not sct.check_if_same_space(fname_data, fname_landmarks):
# sct.printv('ERROR: Data image and landmarks are not in the same space. Please check space and orientation of your files', verbose, 'error')
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# ftmp_template_label_disc = 'template_label_disc.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.run([
'sct_convert', '-i', fname_data, '-o',
os.path.join(path_tmp, ftmp_data)
])
sct.run([
'sct_convert', '-i', fname_seg, '-o',
os.path.join(path_tmp, ftmp_seg)
])
sct.run([
'sct_convert', '-i', fname_landmarks, '-o',
os.path.join(path_tmp, ftmp_label)
])
sct.run([
'sct_convert', '-i', fname_template, '-o',
os.path.join(path_tmp, ftmp_template)
])
sct.run([
'sct_convert', '-i', fname_template_seg, '-o',
os.path.join(path_tmp, ftmp_template_seg)
])
sct_convert.main(args=[
'-i', fname_template_vertebral_labeling, '-o',
os.path.join(path_tmp, ftmp_template_label)
])
if label_type == 'disc':
sct_convert.main(args=[
'-i', fname_template_disc_labeling, '-o',
os.path.join(path_tmp, ftmp_template_label)
])
# sct.run('sct_convert -i '+fname_template_label+' -o '+os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling',
verbose)
sct_label_utils.main(args=[
'-i', ftmp_template_label, '-vert-body', '0', '-o',
ftmp_template_label
])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template',
verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(
sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv(
'ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) +
'\nLabel max from template: ' + str(labels_template[-1].value),
verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv(
'WARNING: Only one label is present. Forcing initial transformation to: '
+ paramreg.steps['0'].dof, 1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
sct.run(
['sct_maths', '-i', 'seg.nii.gz', '-bin', '0.5', '-o', 'seg.nii.gz'])
# smooth segmentation (jcohenadad, issue #613)
# sct.printv('\nSmooth segmentation...', verbose)
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
# jcohenadad: updated 2016-06-16: DO NOT smooth the seg anymore. Issue #
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 0 -o '+add_suffix(ftmp_seg, '_smooth'))
# ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run([
'sct_resample', '-i', ftmp_data, '-mm', '1.0x1.0x1.0', '-x',
'linear', '-o',
add_suffix(ftmp_data, '_1mm')
])
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run([
'sct_resample', '-i', ftmp_seg, '-mm', '1.0x1.0x1.0', '-x',
'linear', '-o',
add_suffix(ftmp_seg, '_1mm')
])
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run([
'sct_image', '-i', ftmp_data, '-setorient', 'RPI', '-o',
add_suffix(ftmp_data, '_rpi')
])
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run([
'sct_image', '-i', ftmp_seg, '-setorient', 'RPI', '-o',
add_suffix(ftmp_seg, '_rpi')
])
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run([
'sct_image', '-i', ftmp_label, '-setorient', 'RPI', '-o',
add_suffix(ftmp_label, '_rpi')
])
ftmp_label = add_suffix(ftmp_label, '_rpi')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(
sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [
coordinates_labels[0].z - offset_crop,
coordinates_labels[-1].z + offset_crop
]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
status_crop, output_crop = sct.run([
'sct_crop_image', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_crop'), '-dim', '2', '-start',
str(cropping_slices[0]), '-end',
str(cropping_slices[1])
], verbose)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
status_crop, output_crop = sct.run([
'sct_crop_image', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_crop'), '-dim', '2', '-bzmax'
], verbose)
cropping_slices = output_crop.split('Dimension 2: ')[1].split(
'\n')[0].split(' ')
# output: segmentation_rpi_crop.nii.gz
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# straighten segmentation
sct.printv(
'\nStraighten the spinal cord using centerline/segmentation...',
verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir,
"warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir,
"warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files = [ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(input_files=cache_input_files, )
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(
cachefile, cache_sig
) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(
fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv(
'Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct.run([
'sct_apply_transfo', '-i', ftmp_seg, '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz',
'-o',
add_suffix(ftmp_seg, '_straight')
])
else:
from sct_straighten_spinalcord import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run([
'sct_concat_transfo', '-w', 'warp_straight2curve.nii.gz', '-d',
ftmp_data, '-o', 'warp_straight2curve.nii.gz'
])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz',
'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv(
'\nRemove unused label on template. Keep only label present in the input label image...',
verbose)
sct.run([
'sct_label_utils', '-i', ftmp_template_label, '-o',
ftmp_template_label, '-remove', ftmp_label
])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run([
'sct_maths', '-i', ftmp_label, '-o',
add_suffix(ftmp_label, '_dilate'), '-dilate', '3'
])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run([
'sct_apply_transfo', '-i', ftmp_label, '-o',
add_suffix(ftmp_label, '_straight'), '-d',
add_suffix(ftmp_seg, '_straight'), '-w',
'warp_curve2straight.nii.gz', '-x', 'nn'
])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
try:
register_landmarks(ftmp_label,
ftmp_template_label,
paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt',
verbose=verbose)
except Exception:
sct.printv(
'ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/',
verbose=verbose,
type='error')
# Concatenate transformations: curve --> straight --> affine
sct.printv(
'\nConcatenate transformations: curve --> straight --> affine...',
verbose)
sct.run([
'sct_concat_transfo', '-w',
'warp_curve2straight.nii.gz,straight2templateAffine.txt', '-d',
'template.nii', '-o', 'warp_curve2straightAffine.nii.gz'
])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run([
'sct_apply_transfo', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_straightAffine'), '-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz'
])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run([
'sct_apply_transfo', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_straightAffine'), '-d', ftmp_template, '-w',
'warp_curve2straightAffine.nii.gz', '-x', 'linear'
])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(round(np.min(points_straight))), int(round(np.max(points_straight)))
sct.run('sct_crop_image -i ' + ftmp_seg + ' -start ' + str(min_point) + ' -end ' + str(max_point) + ' -dim 2 -b 0 -o ' + add_suffix(ftmp_seg, '_black'))
ftmp_seg = add_suffix(ftmp_seg, '_black')
"""
# binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run([
'sct_maths', '-i', ftmp_seg, '-bin', '0.5', '-o',
add_suffix(ftmp_seg, '_bin')
])
ftmp_seg = add_suffix(ftmp_seg, '_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...',
verbose)
sct.run([
'sct_crop_image', '-i', ftmp_template, '-o',
add_suffix(ftmp_template, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run([
'sct_crop_image', '-i', ftmp_template_seg, '-o',
add_suffix(ftmp_template_seg, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run([
'sct_crop_image', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run([
'sct_crop_image', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...',
verbose)
sct.run([
'sct_resample', '-i', ftmp_template, '-o',
add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run([
'sct_resample', '-i', ftmp_template_seg, '-o',
add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run([
'sct_resample', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run([
'sct_resample', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv(
'\nEstimate transformation for step #' + str(i_step) + '...',
verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
# apply transformation from previous step, to use as new src for registration
sct.run([
'sct_apply_transfo', '-i', src, '-d', dest, '-w',
','.join(warp_forward), '-o',
add_suffix(src,
'_regStep' + str(i_step - 1)), '-x', interp_step
], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(
src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...',
verbose)
sct.run([
'sct_concat_transfo', '-w',
'warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward), '-d',
'template.nii', '-o', 'warp_anat2template.nii.gz'
], verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...',
verbose)
warp_inverse.reverse()
if vertebral_alignment:
sct.run([
'sct_concat_transfo', '-w',
','.join(warp_inverse) + ',warp_straight2curve.nii.gz', '-d',
'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
else:
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse) +
',-straight2templateAffine.txt,warp_straight2curve.nii.gz',
'-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run([
'sct_image', '-i', ftmp_data, '-setorient', 'RPI', '-o',
add_suffix(ftmp_data, '_rpi')
])
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run([
'sct_image', '-i', ftmp_seg, '-setorient', 'RPI', '-o',
add_suffix(ftmp_seg, '_rpi')
])
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run([
'sct_image', '-i', ftmp_label, '-setorient', 'RPI', '-o',
add_suffix(ftmp_label, '_rpi')
])
ftmp_label = add_suffix(ftmp_label, '_rpi')
# Remove unused label on template. Keep only label present in the input label image
sct.printv(
'\nRemove unused label on template. Keep only label present in the input label image...',
verbose)
sct.run([
'sct_label_utils', '-i', ftmp_template_label, '-o',
ftmp_template_label, '-remove', ftmp_label
])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue(
) # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x),
int(new_label.y),
int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.setFileName('label_rpi_modif.nii.gz')
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label,
ftmp_label,
paramreg.steps['0'].dof,
fname_affine=warp_forward[0],
verbose=verbose,
path_qc="./")
except Exception:
sct.printv(
'ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/',
verbose=verbose,
type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv(
'\nEstimate transformation for step #' + str(i_step) + '...',
verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run([
'sct_apply_transfo', '-i', src, '-d', dest, '-w',
','.join(warp_forward), '-o',
add_suffix(src,
'_regStep' + str(i_step - 1)), '-x', interp_step
], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(
src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...',
verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_forward), '-d',
'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
sct.printv('\nConcatenate transformations: subject --> template...',
verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse), '-d',
'template.nii', '-o', 'warp_anat2template.nii.gz'
], verbose)
# Apply warping fields to anat and template
sct.run([
'sct_apply_transfo', '-i', 'template.nii', '-o',
'template2anat.nii.gz', '-d', 'data.nii', '-w',
'warp_template2anat.nii.gz', '-crop', '1'
], verbose)
sct.run([
'sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz',
'-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(
os.path.join(path_tmp, "warp_template2anat.nii.gz"),
os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_anat2template.nii.gz"),
os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"),
fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"),
fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(
os.path.join(path_tmp, "warp_curve2straight.nii.gz"),
os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_straight2curve.nii.gz"),
os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "straight_ref.nii.gz"),
os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's',
verbose)
if param.path_qc is not None:
generate_qc(fname_data, fname_template2anat, fname_seg, args,
os.path.abspath(param.path_qc))
sct.display_viewer_syntax([fname_data, fname_template2anat],
verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template],
verbose=verbose)
def checkFile(self, param):
# check if the file exist
sct.printv("Check file existence...", 0)
if self.parser.check_file_exist:
sct.check_file_exist(param, 0)
return param
def main():
# Initialization
fname_warp_final = '' # concatenated transformations
# Check input parameters
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_dest = arguments['-d']
fname_warp_list = arguments['-w']
if '-o' in arguments:
fname_warp_final = arguments['-o']
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# Parse list of warping fields
sct.printv('\nParse list of transformations...', verbose)
use_inverse = []
fname_warp_list_invert = []
for i in range(len(fname_warp_list)):
# Check if inverse matrix is specified with '-' at the beginning of file name
if fname_warp_list[i].find('-') == 0:
use_inverse.append('-i')
fname_warp_list[i] = fname_warp_list[i][1:] # remove '-'
fname_warp_list_invert += [[use_inverse[i], fname_warp_list[i]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[fname_warp_list[i]]]
sct.printv(
' Transfo #' + str(i) + ': ' + use_inverse[i] +
fname_warp_list[i], verbose)
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_dest, verbose)
for i in range(len(fname_warp_list)):
sct.check_file_exist(fname_warp_list[i], verbose)
# Get output folder and file name
if fname_warp_final == '':
path_out, file_out, ext_out = sct.extract_fname(param.fname_warp_final)
else:
path_out, file_out, ext_out = sct.extract_fname(fname_warp_final)
# Check dimension of destination data (cf. issue #1419, #1429)
im_dest = Image(fname_dest)
if im_dest.dim[2] == 1:
dimensionality = '2'
else:
dimensionality = '3'
# Concatenate warping fields
sct.printv('\nConcatenate warping fields...', verbose)
# N.B. Here we take the inverse of the warp list
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x, y: x + y,
fname_warp_list_invert)
cmd = [
'isct_ComposeMultiTransform', dimensionality, 'warp_final' + ext_out,
'-R', fname_dest
] + fname_warp_list_invert
status, output = sct.run(cmd, verbose=verbose, is_sct_binary=True)
# check if output was generated
if not os.path.isfile('warp_final' + ext_out):
sct.printv('ERROR: Warping field was not generated.\n' + output, 1,
'error')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file('warp_final' + ext_out,
os.path.join(path_out, file_out + ext_out))
def main():
# Initialization
path_script = os.path.dirname(__file__)
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# THIS DOES NOT WORK IN MY LAPTOP: path_sct = os.environ['SCT_DIR'] # path to spinal cord toolbox
#path_sct = path_script[:-8] # TODO: make it cleaner!
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
fname_segmentation = ''
name_process = ''
processes = ['centerline', 'csa', 'length']
method_CSA = [
'counting_ortho_plane', 'counting_z_plane', 'ellipse_ortho_plane',
'ellipse_z_plane'
]
name_method = param.name_method
volume_output = param.volume_output
verbose = param.verbose
start_time = time.time()
remove_temp_files = param.remove_temp_files
# spline_smoothing = param.spline_smoothing
step = param.step
smoothing_param = param.smoothing_param
figure_fit = param.figure_fit
name_output = param.name_output
slices = param.slices
vert_lev = param.vertebral_levels
path_to_template = param.path_to_template
# Parameters for debug mode
if param.debug:
fname_segmentation = '/Users/julien/data/temp/sct_example_data/t2/t2_seg.nii.gz' #path_sct+'/testing/data/errsm_23/t2/t2_segmentation_PropSeg.nii.gz'
name_process = 'csa'
verbose = 1
volume_output = 1
remove_temp_files = 0
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],
'hi:p:m:b:l:r:s:t:f:o:v:z:a:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
fname_segmentation = arg
elif opt in ("-p"):
name_process = arg
elif opt in ("-m"):
name_method = arg
elif opt in ('-b'):
volume_output = int(arg)
elif opt in ('-l'):
vert_lev = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-s'):
smoothing_param = int(arg)
elif opt in ('-f'):
figure_fit = int(arg)
elif opt in ('-o'):
name_output = arg
elif opt in ('-t'):
path_to_template = arg
elif opt in ('-v'):
verbose = int(arg)
volume_output = 1
elif opt in ('-z'):
slices = arg
elif opt in ('-a'):
param.algo_fitting = str(arg)
# display usage if a mandatory argument is not provided
if fname_segmentation == '' or name_process == '':
usage()
# display usage if the requested process is not available
if name_process not in processes:
usage()
# display usage if incorrect method
if name_process == 'csa' and (name_method not in method_CSA):
usage()
# display usage if no method provided
if name_process == 'csa' and method_CSA == '':
usage()
# update fields
param.verbose = verbose
# check existence of input files
sct.check_file_exist(fname_segmentation)
# print arguments
print '\nCheck parameters:'
print '.. segmentation file: ' + fname_segmentation
if name_process == 'centerline':
fname_output = extract_centerline(fname_segmentation,
remove_temp_files,
name_output=name_output,
verbose=param.verbose,
algo_fitting=param.algo_fitting)
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview ' + fname_output + ' &\n', param.verbose, 'info')
if name_process == 'csa':
volume_output = 1
compute_csa(fname_segmentation,
name_method,
volume_output,
verbose,
remove_temp_files,
step,
smoothing_param,
figure_fit,
name_output,
slices,
vert_lev,
path_to_template,
algo_fitting=param.algo_fitting,
type_window=param.type_window,
window_length=param.window_length)
sct.printv('\nDone!', param.verbose)
if (volume_output):
sct.printv('Output CSA volume: ' + name_output, param.verbose,
'info')
if slices or vert_lev:
sct.printv('Output CSA file (averaged): csa_mean.txt',
param.verbose, 'info')
sct.printv('Output CSA file (all slices): ' + param.fname_csa + '\n',
param.verbose, 'info')
if name_process == 'length':
result_length = compute_length(fname_segmentation,
remove_temp_files,
verbose=verbose)
sct.printv(
'\nLength of the segmentation = ' + str(round(result_length, 2)) +
' mm\n', verbose, 'info')
def main():
print '\n\n\n==================================================='
print ' Running: sct_labeling'
print '===================================================\n'
# Initialization
start_time = time.time()
param = param_class()
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:c:l:m:a:s:r:o:g:v:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
param.input_anat = arg
elif opt in ('-c'):
param.contrast = arg
elif opt in ('-l'):
param.input_centerline = arg
elif opt in ('-m'):
param.mean_distance_mat = arg
elif opt in ('-a'):
param.shift_AP = int(arg)
elif opt in ('-s'):
param.size_AP = int(arg)
elif opt in ('-r'):
param.size_RL = int(arg)
elif opt in ('-o'):
param.output_path = arg
elif opt in ('-g'):
param.plot_graph = int(arg)
elif opt in ('-v'):
param.verbose = int(arg)
# Display usage if a mandatory argument is not provided
if param.input_anat == '' or param.contrast=='' or param.input_centerline=='' or param.mean_distance_mat=='':
print '\n \n All mandatory arguments are not provided \n \n'
usage()
# Extract path, file and extension
input_path, file_data, ext_data = sct.extract_fname(param.input_anat)
if param.output_path=='': param.output_path = os.getcwd() + '/'
print 'Input File:',param.input_anat
print 'Center_line file:',param.input_centerline
print 'Contrast:',param.contrast
print 'Mat File:',param.mean_distance_mat
# check existence of input files
sct.check_file_exist(param.input_anat)
sct.check_file_exist(param.input_centerline)
sct.check_file_exist(param.mean_distance_mat)
#==================================================
# Reorientation of the data if needed
#==================================================
command = 'fslhd ' + param.input_anat
result = commands.getoutput(command)
orientation = result[result.find('qform_xorient')+15] + result[result.find('qform_yorient')+15] + result[result.find('qform_zorient')+15]
if orientation!='ASR':
sct.printv('\nReorient input volume to AP SI RL orientation...',param.verbose)
sct.run(sct.fsloutput + 'fslswapdim ' + param.input_anat + ' AP SI RL ' + input_path + 'tmp.anat_orient')
sct.run(sct.fsloutput + 'fslswapdim ' + param.input_centerline + ' AP SI RL ' + input_path + 'tmp.centerline_orient')
param.input_anat = input_path + 'tmp.anat_orient.nii'
param.centerline = input_path + 'tmp.centerline_orient.nii'
if param.plot_graph:
import pylab as pl
#loading images
sct.printv('\nLoading Images...',verbose)
anat_file = nibabel.load(param.input_anat)
anat = anat_file.get_data()
hdr = anat_file.get_header()
dims = hdr['dim']
scales = hdr['pixdim']
centerline_file = nibabel.load(param.input_centerline)
centerline = centerline_file.get_data()
shift_AP = param.shift_AP*scales[1]
size_AP = param.size_AP*scales[1]
size_RL = param.size_RL*scales[3]
np.uint16(anat)
if param.contrast == 'T1':
labeling_vertebrae_T1(param,anat,hdr,dims,scales)
else:
labeling_vertebrae_T2(param,anat,hdr,dims,scales)
#generating output file
if ext_data == '.nii.gz':
os.system('fslchfiletype NIFTI_GZ '+ param.output_path + param.contrast + '_centerline.nii.gz')
# display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s'
def main():
# get path of the toolbox
status, path_sct = getstatusoutput('echo $SCT_DIR')
#print path_sct
#Initialization
fname = ''
landmark = ''
verbose = param.verbose
output_name = 'aligned.nii.gz'
template_landmark = ''
final_warp = param.final_warp
compose = param.compose
transfo = 'affine'
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:l:o:R:t:w:c:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-l"):
landmark = arg
elif opt in ("-o"):
output_name = arg
elif opt in ("-R"):
template_landmark = arg
elif opt in ("-t"):
transfo = arg
elif opt in ("-w"):
final_warp = arg
elif opt in ("-c"):
compose = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname == '' or landmark == '' or template_landmark == '' :
usage()
if final_warp not in ['','spline','NN']:
usage()
if transfo not in ['affine','bspline','SyN']:
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
sct.check_file_exist(landmark)
sct.check_file_exist(template_landmark)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Verbose ........................... '+str(verbose)
if transfo == 'affine':
print 'Creating cross using input landmarks\n...'
sct.run('sct_label_utils -i ' + landmark + ' -o ' + 'cross_native.nii.gz -t cross ' )
print 'Creating cross using template landmarks\n...'
sct.run('sct_label_utils -i ' + template_landmark + ' -o ' + 'cross_template.nii.gz -t cross ' )
print 'Computing affine transformation between subject and destination landmarks\n...'
os.system('isct_ANTSUseLandmarkImagesToGetAffineTransform cross_template.nii.gz cross_native.nii.gz affine n2t.txt')
warping = 'n2t.txt'
elif transfo == 'SyN':
warping = 'warp_subject2template.nii.gz'
tmp_name = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+tmp_name)
tmp_abs_path = os.path.abspath(tmp_name)
sct.run('cp ' + landmark + ' ' + tmp_abs_path)
os.chdir(tmp_name)
# sct.run('sct_label_utils -i '+landmark+' -t dist-inter')
# sct.run('sct_label_utils -i '+template_landmark+' -t plan -o template_landmarks_plan.nii.gz -c 5')
# sct.run('sct_crop_image -i template_landmarks_plan.nii.gz -o template_landmarks_plan_cropped.nii.gz -start 0.35,0.35 -end 0.65,0.65 -dim 0,1')
# sct.run('sct_label_utils -i '+landmark+' -t plan -o landmarks_plan.nii.gz -c 5')
# sct.run('sct_crop_image -i landmarks_plan.nii.gz -o landmarks_plan_cropped.nii.gz -start 0.35,0.35 -end 0.65,0.65 -dim 0,1')
# sct.run('isct_antsRegistration --dimensionality 3 --transform SyN[0.5,3,0] --metric MeanSquares[template_landmarks_plan_cropped.nii.gz,landmarks_plan_cropped.nii.gz,1] --convergence 400x200 --shrink-factors 4x2 --smoothing-sigmas 4x2mm --restrict-deformation 0x0x1 --output [landmarks_reg,landmarks_reg.nii.gz] --interpolation NearestNeighbor --float')
# sct.run('isct_c3d -mcs landmarks_reg0Warp.nii.gz -oo warp_vecx.nii.gz warp_vecy.nii.gz warp_vecz.nii.gz')
# sct.run('isct_c3d warp_vecz.nii.gz -resample 200% -o warp_vecz_r.nii.gz')
# sct.run('isct_c3d warp_vecz_r.nii.gz -smooth 0x0x3mm -o warp_vecz_r_sm.nii.gz')
# sct.run('sct_crop_image -i warp_vecz_r_sm.nii.gz -o warp_vecz_r_sm_line.nii.gz -start 0.5,0.5 -end 0.5,0.5 -dim 0,1 -b 0')
# sct.run('sct_label_utils -i warp_vecz_r_sm_line.nii.gz -t plan_ref -o warp_vecz_r_sm_line_extended.nii.gz -c 0 -r '+template_landmark)
# sct.run('isct_c3d '+template_landmark+' warp_vecx.nii.gz -reslice-identity -o warp_vecx_res.nii.gz')
# sct.run('isct_c3d '+template_landmark+' warp_vecy.nii.gz -reslice-identity -o warp_vecy_res.nii.gz')
# sct.run('isct_c3d warp_vecx_res.nii.gz warp_vecy_res.nii.gz warp_vecz_r_sm_line_extended.nii.gz -omc 3 '+warping) # no x?
#new
#put labels of the subject at the center of the image (for plan xOy)
import nibabel
from copy import copy
file_labels_input = nibabel.load(landmark)
hdr_labels_input = file_labels_input.get_header()
data_labels_input = file_labels_input.get_data()
data_labels_middle = copy(data_labels_input)
data_labels_middle *= 0
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(landmark)
X,Y,Z = data_labels_input.nonzero()
x_middle = int(round(nx/2.0))
y_middle = int(round(ny/2.0))
for i in range(len(Z)):
data_labels_middle[x_middle, y_middle, Z[i]] = data_labels_input[X[i], Y[i], Z[i]]
img = nibabel.Nifti1Image(data_labels_middle, None, hdr_labels_input)
nibabel.save(img, 'labels_input_middle_xy.nii.gz')
#put labels of the template at the center of the image (for plan xOy) #probably not necessary as already done by average labels
file_labels_template = nibabel.load(template_landmark)
hdr_labels_template = file_labels_template.get_header()
data_labels_template = file_labels_template.get_data()
data_template_middle = copy(data_labels_template)
data_template_middle *= 0
x,y,z = data_labels_template.nonzero()
for i in range(len(Z)):
data_template_middle[x_middle, y_middle, z[i]] = data_labels_template[x[i], y[i], z[i]]
img_template = nibabel.Nifti1Image(data_template_middle, None, hdr_labels_template)
nibabel.save(img_template, 'labels_template_middle_xy.nii.gz')
#estimate Bspline transform to register to template
sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField labels_template_middle_xy.nii.gz labels_input_middle_xy.nii.gz '+ warping+' 40*40*1 2 5 1')
# select centerline of warping field according to z and extend it
sct.run('isct_c3d -mcs '+warping+' -oo warp_vecx.nii.gz warp_vecy.nii.gz warp_vecz.nii.gz')
#sct.run('isct_c3d warp_vecz.nii.gz -resample 200% -o warp_vecz_r.nii.gz')
#sct.run('isct_c3d warp_vecz.nii.gz -smooth 0x0x3mm -o warp_vecz_r_sm.nii.gz')
sct.run('sct_crop_image -i warp_vecz.nii.gz -o warp_vecz_r_sm_line.nii.gz -start 0.5,0.5 -end 0.5,0.5 -dim 0,1 -b 0')
sct.run('sct_label_utils -i warp_vecz_r_sm_line.nii.gz -t plan_ref -o warp_vecz_r_sm_line_extended.nii.gz -r '+template_landmark)
sct.run('isct_c3d '+template_landmark+' warp_vecx.nii.gz -reslice-identity -o warp_vecx_res.nii.gz')
sct.run('isct_c3d '+template_landmark+' warp_vecy.nii.gz -reslice-identity -o warp_vecy_res.nii.gz')
sct.run('isct_c3d warp_vecx_res.nii.gz warp_vecy_res.nii.gz warp_vecz_r_sm_line_extended.nii.gz -omc 3 '+warping)
# check results
#dilate first labels
sct.run('fslmaths labels_input_middle_xy.nii.gz -dilF landmark_dilated.nii.gz') #new
sct.run('sct_apply_transfo -i landmark_dilated.nii.gz -o label_moved.nii.gz -d labels_template_middle_xy.nii.gz -w '+warping+' -x nn')
#undilate
sct.run('sct_label_utils -i label_moved.nii.gz -t cubic-to-point -o label_moved_2point.nii.gz')
sct.run('sct_label_utils -i labels_template_middle_xy.nii.gz -r label_moved_2point.nii.gz -o template_removed.nii.gz -t remove')
#end new
# check results
#dilate first labels
#sct.run('fslmaths '+landmark+' -dilF landmark_dilated.nii.gz') #old
#sct.run('sct_apply_transfo -i landmark_dilated.nii.gz -o label_moved.nii.gz -d '+template_landmark+' -w '+warping+' -x nn') #old
#undilate
#sct.run('sct_label_utils -i label_moved.nii.gz -t cubic-to-point -o label_moved_2point.nii.gz') #old
#sct.run('sct_label_utils -i '+template_landmark+' -r label_moved_2point.nii.gz -o template_removed.nii.gz -t remove') #old
# # sct.run('sct_apply_transfo -i '+landmark+' -o label_moved.nii.gz -d '+template_landmark+' -w '+warping+' -x nn')
# # sct.run('sct_label_utils -i '+template_landmark+' -r label_moved.nii.gz -o template_removed.nii.gz -t remove')
# # status, output = sct.run('sct_label_utils -i label_moved.nii.gz -r template_removed.nii.gz -t MSE')
status, output = sct.run('sct_label_utils -i label_moved_2point.nii.gz -r template_removed.nii.gz -t MSE')
sct.printv(output,1,'info')
remove_temp_files = False
if os.path.isfile('error_log_label_moved.txt'):
remove_temp_files = False
with open('log.txt', 'a') as log_file:
log_file.write('Error for '+fname+'\n')
# Copy warping into parent folder
sct.run('cp '+ warping+' ../'+warping)
os.chdir('..')
if remove_temp_files:
sct.run('rm -rf '+tmp_name)
# if transfo == 'bspline' :
# print 'Computing bspline transformation between subject and destination landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField cross_template.nii.gz cross_native.nii.gz warp_ntotemp.nii.gz 5x5x5 3 2 0')
# warping = 'warp_ntotemp.nii.gz'
# if final_warp == '' :
# print 'Apply transfo to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping + ' -n Linear')
# if final_warp == 'NN':
# print 'Apply transfo to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping + ' -n NearestNeighbor')
if final_warp == 'spline':
print 'Apply transfo to input image\n...'
sct.run('sct_apply_transfo -i ' + fname + ' -o ' + output_name + ' -d ' + template_landmark + ' -w ' + warping + ' -x spline')
# Remove warping
os.remove(warping)
# if compose :
# print 'Computing affine transformation between subject and destination landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetAffineTransform cross_template.nii.gz cross_native.nii.gz affine n2t.txt')
# warping_affine = 'n2t.txt'
# print 'Apply transfo to input landmarks\n...'
# sct.run('isct_antsApplyTransforms 3 ' + cross_native + ' cross_affine.nii.gz -r ' + template_landmark + ' -t ' + warping_affine + ' -n NearestNeighbor')
# print 'Computing transfo between moved landmarks and template landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField cross_template.nii.gz cross_affine.nii.gz warp_affine2temp.nii.gz 5x5x5 3 2 0')
# warping_bspline = 'warp_affine2temp.nii.gz'
# print 'Composing transformations\n...'
# sct.run('isct_ComposeMultiTransform 3 warp_full.nii.gz -r ' + template_landmark + ' ' + warping_bspline + ' ' + warping_affine)
# warping_concat = 'warp_full.nii.gz'
# if final_warp == '' :
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n Linear')
# if final_warp == 'NN':
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n NearestNeighbor')
# if final_warp == 'spline':
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n BSpline[3]')
print '\nFile created : ' + output_name
def get_parser():
param_default = Param()
# Read .txt files referencing the labels (for extended usage description)
file_label = os.path.join(param_default.path_label,
param_default.file_info_label)
sct.check_file_exist(file_label, 0)
default_info_label = open(file_label, 'r')
label_references = default_info_label.read()
default_info_label.close()
description = (f"This program extracts metrics (e.g., DTI or MTR) within labels. Labels could be a single file or "
f"a folder generated with 'sct_warp_template' and containing multiple label files and a label "
f"description file (info_label.txt). The labels should be in the same space coordinates as the "
f"input image.\n"
f"\n"
f"To list white matter atlas labels: {os.path.basename(__file__)} -f "
f"{os.path.join(path_sct, 'data', 'atlas')}\n"
f"\n"
f"To compute FA within labels 0, 2 and 3 within vertebral levels C2 to C7 using binary method: "
f"{os.path.basename(__file__)} -i dti_FA.nii.gz -f label/atlas -l 0,2,3 -v 2:7 -m bin\n")
if label_references != '':
description += (f"\nTo compute average MTR in a region defined by a single label file (could be binary or 0-1 "
f"weighted mask) between slices 1 and 4: {os.path.basename(__file__)} -i mtr.nii.gz -f "
f"my_mask.nii.gz -z 1:4 -m wa\n"
f"List of labels in {file_label}:\n"
f"--------------------------------------------------------------------------------------\n"
f"{label_references}\n"
f"--------------------------------------------------------------------------------------\n")
parser = argparse.ArgumentParser(
description=description,
formatter_class=SmartFormatter,
add_help=None,
prog=os.path.basename(__file__).strip(".py")
)
mandatory = parser.add_argument_group("\nMANDATORY ARGUMENTS")
mandatory.add_argument(
'-i',
metavar=Metavar.file,
required=True,
help="Image file to extract metrics from. Example: FA.nii.gz"
)
optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS")
optional.add_argument(
"-h",
"--help",
action="help",
help="Show this help message and exit."
)
optional.add_argument(
'-f',
metavar=Metavar.folder,
default=os.path.join("label", "atlas"),
help=(f"Single label file, or folder that contains WM tract labels."
f"Example: {os.path.join(path_sct, 'data', 'atlas')}")
)
optional.add_argument(
'-l',
metavar=Metavar.str,
default='',
help="Label IDs to extract the metric from. Default = all labels. Separate labels with ','. To select a group "
"of consecutive labels use ':'. Example: 1:3 is equivalent to 1,2,3. Maximum Likelihood (or MAP) is "
"computed using all tracts, but only values of the selected tracts are reported."
)
optional.add_argument(
'-method',
choices=['ml', 'map', 'wa', 'bin', 'max'],
default=param_default.method,
help="R|Method to extract metrics.\n"
" - ml: maximum likelihood (only use with well-defined regions and low noise)\n"
" N.B. ONLY USE THIS METHOD WITH THE WHITE MATTER ATLAS! The sum of all tracts should be 1 in "
"all voxels (the algorithm doesn't normalize the atlas).\n"
" - map: maximum a posteriori. Mean priors are estimated by maximum likelihood within three clusters "
"(white matter, gray matter and CSF). Tract and noise variance are set with flag -p.\n"
" N.B. ONLY USE THIS METHOD WITH THE WHITE MATTER ATLAS! The sum of all tracts should be 1 in "
"all voxels (the algorithm doesn't normalize the atlas).\n"
" - wa: weighted average\n"
" - bin: binarize mask (threshold=0.5)\n"
" - max: for each z-slice of the input data, extract the max value for each slice of the input data."
)
optional.add_argument(
'-append',
type=int,
choices=(0, 1),
default=0,
help="Whether to append results as a new line in the output csv file instead of overwriting it. 0 = no, 1 = yes"
)
optional.add_argument(
'-combine',
type=int,
choices=(0, 1),
default=0,
help="Whether to combine multiple labels into a single estimation. 0 = no, 1 = yes"
)
optional.add_argument(
'-o',
metavar=Metavar.file,
default=param_default.fname_output,
help="R|File name of the output result file collecting the metric estimation results. Include the '.csv' "
"file extension in the file name. Example: extract_metric.csv"
)
optional.add_argument(
'-output-map',
metavar=Metavar.file,
default='',
help="File name for an image consisting of the atlas labels multiplied by the estimated metric values "
"yielding the metric value map, useful to assess the metric estimation and especially partial volume "
"effects."
)
optional.add_argument(
'-z',
metavar=Metavar.str,
default=param_default.slices_of_interest,
help="R|Slice range to estimate the metric from. First slice is 0. Example: 5:23\n"
"You can also select specific slices using commas. Example: 0,2,3,5,12'"
)
optional.add_argument(
'-perslice',
type=int,
choices=(0, 1),
default=param_default.perslice,
help="R|Whether to output one metric per slice instead of a single output metric. 0 = no, 1 = yes.\n"
"Please note that when methods ml or map are used, outputting a single metric per slice and then "
"averaging them all is not the same as outputting a single metric at once across all slices."
)
optional.add_argument(
'-vert',
metavar=Metavar.str,
default=param_default.vertebral_levels,
help="Vertebral levels to estimate the metric across. Example: 2:9 (for C2 to T2)"
)
optional.add_argument(
'-vertfile',
metavar=Metavar.file,
default="./label/template/PAM50_levels.nii.gz",
help="Vertebral labeling file. Only use with flag -vert. Example: PAM50_levels.nii.gz"
)
optional.add_argument(
'-perlevel',
type=int,
metavar=Metavar.int,
default=0,
help="R|Whether to output one metric per vertebral level instead of a single output metric. 0 = no, 1 = yes.\n"
"Please note that this flag needs to be used with the -vert option."
)
optional.add_argument(
'-v',
choices=("0", "1"),
default="1",
help="Verbose. 0 = nothing, 1 = expanded"
)
advanced = parser.add_argument_group("\nFOR ADVANCED USERS")
advanced.add_argument(
'-param',
metavar=Metavar.str,
default='',
help="R|Advanced parameters for the 'map' method. Separate with comma. All items must be listed (separated "
"with comma).\n"
" - #1: standard deviation of metrics across labels\n"
" - #2: standard deviation of the noise (assumed Gaussian)"
)
advanced.add_argument(
'-fix-label',
metavar=Metavar.list,
type=list_type(',', str),
default='',
help="When using ML or MAP estimations, if you do not want to estimate the metric in one label and fix its "
"value to avoid effects on other labels, specify <label_ID>,<metric_value. Example: -fix-label 36,0 "
"(Fix the CSF value)"
)
advanced.add_argument(
'-norm-file',
metavar=Metavar.file,
default='',
help='Filename of the label by which the user wants to normalize.'
)
advanced.add_argument(
'-norm-method',
choices=['sbs', 'whole'],
default='',
help="R|Method to use for normalization:\n"
" - sbs: normalization slice-by-slice\n"
" - whole: normalization by the metric value in the whole label for all slices."
)
advanced.add_argument(
'-mask-weighted',
metavar=Metavar.file,
default='',
help="Nifti mask to weight each voxel during ML or MAP estimation. Example: PAM50_wm.nii.gz"
)
advanced.add_argument(
'-discard-neg-val',
choices=('0', '1'),
default='0',
help='Whether to discard voxels with negative value when computing metrics statistics. 0 = no, 1 = yes'
)
return parser
def main():
# Initialization
fname_data = ''
suffix_out = '_crop'
remove_temp_files = param.remove_temp_files
verbose = param.verbose
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
remove_temp_files = param.remove_temp_files
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = path_sct+'/testing/data/errsm_23/t2/t2.nii.gz'
remove_temp_files = 0
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:r:v:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# print arguments
print '\nCheck parameters:'
print ' data ................... '+fname_data
print
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data+suffix_out, ext_data
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")+'/'
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.run('isct_c3d '+fname_data+' -o '+path_tmp+'data.nii')
# go to tmp folder
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
set_orientation('data.nii', 'RPI', 'data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx/2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title('Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.')
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n', 1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.setFileName('data_rpi_crop.nii')
nii.save()
# come back to parent folder
os.chdir('..')
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'data_rpi_crop.nii', path_out+file_out+ext_out)
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
# to view results
print '\nDone! To view results, type:'
print 'fslview '+path_out+file_out+ext_out+' &'
print
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param_centerline = ParamCenterline(
algo_fitting=arguments['-centerline-algo'],
smooth=arguments['-centerline-smooth'])
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg, param_centerline)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-w', 'warp_curve2straight.nii.gz',
'-d', 'straight_ref.nii.gz',
'-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.param_centerline = param_centerline
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct_concat_transfo.main(args=[
'-w', 'warp_straight2curve.nii.gz',
'-d', ftmp_data,
'-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_label,
'-o', add_suffix(ftmp_label, '_straight'),
'-d', add_suffix(ftmp_seg, '_straight'),
'-w', 'warp_curve2straight.nii.gz',
'-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct_concat_transfo.main(args=[
'-w', ['warp_curve2straight.nii.gz', 'straight2templateAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_data,
'-o', add_suffix(ftmp_data, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-o', add_suffix(ftmp_seg, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz',
'-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct_apply_transfo.main(args=[
'-i', src_seg,
'-d', dest_seg,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations: anat --> template
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
warp_forward.insert(0, 'warp_curve2straightAffine.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Concatenate transformations: template --> anat
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
else:
warp_inverse.append('straight2templateAffine.txt')
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['straight2templateAffine.txt'],
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['template2subjectAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def crop_with_gui(self):
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Initialization
fname_data = self.input_filename
suffix_out = '_crop'
remove_temp_files = self.rm_tmp_files
verbose = self.verbose
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
verbose)
# check if 4D data
if not nt == 1:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) +
': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' data ................... ' + fname_data)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
path_tmp = sct.tmp_create() + "/"
# copy files into tmp folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
Image('data.nii').change_orientation("RPI").save('data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title(
'Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.'
)
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv(
'\nERROR: You have to select two points. Exit program.\n', 1,
'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.absolutepath = 'data_rpi_crop.nii'
nii.save()
# come back
os.chdir(curdir)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "data_rpi_crop.nii"),
os.path.join(path_out, file_out + ext_out))
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
sct.display_viewer_syntax(
files=[os.path.join(path_out, file_out + ext_out)])
def get_slices_matching_with_vertebral_levels(metric_data,fname_tracts,vert_levels_list=None):
"""Return the slices of the input image corresponding to the vertebral levels given as argument."""
# check existence of "vertebral_labeling.nii.gz" file
fname_vertebral_labeling = fname_tracts + '/vertebral_labeling.nii.gz'
sct.check_file_exist(fname_vertebral_labeling)
# Read files vertebral_labeling.nii.gz
print '\nRead files vertebral_labeling.nii.gz...'
# Load vertebral_labeling.nii.gz
file_vert_labeling = load(fname_vertebral_labeling)
# Make data in array format
data_vert_labeling = file_vert_labeling.get_data()
# Extract metric data size X, Y, Z
[mx, my, mz] = metric_data.shape
# Extract vertebral labeling data size X, Y, Z
[vx, vy, vz] = data_vert_labeling.shape
# Initialisation of check error flag
exit_program = 0
# Check if sizes along X are the same
if mx != vx:
print '\tERROR: Size of vertebral_labeling.nii.gz along X is not the same as the metric data.'
exit_program = 1
# Check if sizes along Y are the same
if my != vy:
print '\tERROR: Size of vertebral_labeling.nii.gz along Y is not the same as the metric data.'
exit_program = 1
# Check if sizes along Z are the same
if mz != vz:
print '\tERROR: Size of vertebral_labeling.nii.gz along Z is not the same as the metric data.'
exit_program = 1
# Compute the minimum and maximum vertebral levels available in the input image
min_vert_level, max_vert_level = int(numpy.amin(concatenate(data_vert_labeling,axis=None))), int(numpy.amax(concatenate(data_vert_labeling,axis=None)))
if vert_levels_list!=None:
# Check if the vertebral levels selected are available in the input image
if (vert_levels_list[0] < min_vert_level or vert_levels_list[1] > max_vert_level):
print '\tERROR: The vertebral levels you selected are not available in the input image.'
print 'Minimum level asked: '+ str(vert_levels_list[0])
print '...minimum level available in the input image: '+str(min_vert_level)
print 'Maximum level asked: '+ str(vert_levels_list[1])
print '...maximum level available in the input image: '+str(max_vert_level)
exit_program = 1
# Exit program if error is detect in sizes
if exit_program == 1 :
print '\nExit program.\n'
sys.exit(2)
# Extract the X, Y, Z positions of voxels belonging to the first vertebral level
X_bottom_level, Y_bottom_level, Z_bottom_level = (data_vert_labeling==vert_levels_list[0]).nonzero()
# Record the bottom of slice of this level
slice_min_bottom = min(Z_bottom_level)
# Extract the X, Y, Z positions of voxels belonging to the last vertebral level
X_top_level, Y_top_level, Z_top_level = (data_vert_labeling==vert_levels_list[1]).nonzero()
# Record the top slice of this level
slice_max_top = max(Z_top_level)
# Take into account the case where the ordering of the slice is reversed compared to the ordering of the vertebral level
if slice_min_bottom > slice_max_top:
slice_min = min(Z_top_level)
slice_max = max(Z_bottom_level)
else:
slice_min = min(Z_bottom_level)
slice_max = max(Z_top_level)
# Return the slice numbers in the right format
return str(slice_min)+':'+str(slice_max)
else:
# Exit program if error is detect in sizes
if exit_program == 1 :
print '\nExit program.\n'
sys.exit(2)
# Return the minimum and maximum vertebral levels available in the input image
return [min_vert_level, max_vert_level]
def main():
#Initialization
fname = ''
landmarks_native = ''
#landmarks_template = path_sct + '/dev/template_creation/template_landmarks-mm.nii.gz'
landmarks_template = path_sct + '/dev/template_creation/landmark_native.nii.gz'
reference = path_sct + '/dev/template_creation/template_shape.nii.gz'
verbose = param.verbose
interpolation_method = 'spline'
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:n:t:R:v:a:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-n"):
landmarks_native = arg
elif opt in ("-t"):
landmarks_template = arg
elif opt in ("-R"):
reference = arg
elif opt in ('-v'):
verbose = int(arg)
elif opt in ('-a'):
interpolation_method = str(arg)
# display usage if a mandatory argument is not provided
if fname == '':
usage()
# check existence of input files
print '\nCheck if file exists ...'
sct.check_file_exist(fname)
sct.check_file_exist(landmarks_native)
sct.check_file_exist(landmarks_template)
sct.check_file_exist(reference)
path_input, file_input, ext_input = sct.extract_fname(fname)
output_name = path_input + file_input + '_2temp' + ext_input
print output_name
transfo = 'native2temp.txt'
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... ' + fname
print ' Landmarks in native space ...................... ' + landmarks_native
print ' Landmarks in template space ...................... ' + landmarks_template
print ' Reference ...................... ' + reference
print ' Verbose ........................... ' + str(verbose)
print '\nEstimate rigid transformation between paired landmarks...'
sct.run('isct_ANTSUseLandmarkImagesToGetAffineTransform ' +
landmarks_template + ' ' + landmarks_native + ' affine ' + transfo)
# Apply rigid transformation
print '\nApply affine transformation to native landmarks...'
sct.run('sct_apply_transfo -i ' + fname + ' -o ' + output_name + ' -d ' +
reference + ' -w ' + transfo + ' -x ' + interpolation_method)
# sct.run('WarpImageMultiTransform 3 ' + fname + ' ' + output_name + ' -R ' + reference + ' ' + transfo)
print '\nFile created : ' + output_name
def main():
# initialization
start_time = time.time()
path_out = '.'
param_user = ''
# reducing the number of CPU used for moco (see issue #201)
os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "1"
# get path of the toolbox
status, param.path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Parameters for debug mode
if param.debug:
# get path of the testing data
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
param.fname_data = path_sct_data+'/dmri/dmri.nii.gz'
param.fname_bvecs = path_sct_data+'/dmri/bvecs.txt'
param.fname_mask = path_sct_data+'/dmri/dmri.nii.gz'
param.remove_tmp_files = 0
param.verbose = 1
param.run_eddy = 0
param.otsu = 0
param.group_size = 5
param.iterative_averaging = 1
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:a:b:e:f:g:m:o:p:r:t:v:x:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-a'):
param.fname_bvals = arg
elif opt in ('-b'):
param.fname_bvecs = arg
elif opt in ('-e'):
param.run_eddy = int(arg)
elif opt in ('-f'):
param.spline_fitting = int(arg)
elif opt in ('-g'):
param.group_size = int(arg)
elif opt in ('-i'):
param.fname_data = arg
elif opt in ('-m'):
param.fname_mask = arg
elif opt in ('-o'):
path_out = arg
elif opt in ('-p'):
param_user = arg
elif opt in ('-r'):
param.remove_tmp_files = int(arg)
elif opt in ('-t'):
param.otsu = int(arg)
elif opt in ('-v'):
param.verbose = int(arg)
elif opt in ('-x'):
param.interp = arg
# display usage if a mandatory argument is not provided
if param.fname_data == '' or param.fname_bvecs == '':
sct.printv('ERROR: All mandatory arguments are not provided. See usage.', 1, 'error')
usage()
# parse argument for param
if not param_user == '':
param.param = param_user.replace(' ', '').split(',') # remove spaces and parse with comma
# TODO: check integrity of input
# param.param = [i for i in range(len(param_user))]
del param_user
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' input file ............'+param.fname_data, param.verbose)
sct.printv(' bvecs file ............'+param.fname_bvecs, param.verbose)
sct.printv(' bvals file ............'+param.fname_bvals, param.verbose)
sct.printv(' mask file .............'+param.fname_mask, param.verbose)
# check existence of input files
sct.printv('\nCheck file existence...', param.verbose)
sct.check_file_exist(param.fname_data, param.verbose)
sct.check_file_exist(param.fname_bvecs, param.verbose)
if not param.fname_bvals == '':
sct.check_file_exist(param.fname_bvals, param.verbose)
if not param.fname_mask == '':
sct.check_file_exist(param.fname_mask, param.verbose)
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
param.fname_bvecs = os.path.abspath(param.fname_bvecs)
if param.fname_bvals != '':
param.fname_bvals = os.path.abspath(param.fname_bvals)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_mask, file_mask, ext_mask = sct.extract_fname(param.fname_mask)
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, param.verbose)
# Copying input data to tmp folder
# NB: cannot use c3d here because c3d cannot convert 4D data.
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
sct.run('cp '+param.fname_data+' '+path_tmp+'dmri'+ext_data, param.verbose)
sct.run('cp '+param.fname_bvecs+' '+path_tmp+'bvecs.txt', param.verbose)
if param.fname_mask != '':
sct.run('cp '+param.fname_mask+' '+path_tmp+'mask'+ext_mask, param.verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert dmri to nii format
convert('dmri'+ext_data, 'dmri.nii')
# update field in param (because used later).
# TODO: make this cleaner...
if param.fname_mask != '':
param.fname_mask = 'mask'+ext_mask
# run moco
dmri_moco(param)
# come back to parent folder
os.chdir('..')
# Generate output files
path_out = sct.slash_at_the_end(path_out, 1)
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'dmri'+param.suffix+'.nii', path_out+file_data+param.suffix+ext_data, param.verbose)
sct.generate_output_file(path_tmp+'b0_mean.nii', path_out+'b0'+param.suffix+'_mean'+ext_data, param.verbose)
sct.generate_output_file(path_tmp+'dwi_mean.nii', path_out+'dwi'+param.suffix+'_mean'+ext_data, param.verbose)
# Delete temporary files
if param.remove_tmp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', param.verbose)
#To view results
sct.printv('\nTo view results, type:', param.verbose)
sct.printv('fslview -m ortho,ortho '+param.path_out+file_data+param.suffix+' '+file_data+' &\n', param.verbose, 'info')
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
fwhm = param.fwhm
width=param.width
remove_temp_files = param.remove_temp_files
start_time = time.time()
verbose = param.verbose
# extract path of the script
path_script = os.path.dirname(__file__) + '/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_anat = '/home/django/ibouchard/errsm_22_t2_cropped_rpi.nii.gz'
fname_centerline = '/home/django/ibouchard//errsm_22_t2_cropped_centerline.nii.gz'
fwhm=1
width=20
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:c:f:w:r:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-f'):
fwhm = int(arg)
elif opt in ('w'):
width=int(arg)
elif opt in ('-r'):
remove_temp_files = int(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# extract path/file/extension
path_centerline, file_centerline, ext_centerline = sct.extract_fname(fname_centerline)
# Display arguments
print '\nCheck input arguments...'
print '.. Anatomical image: ' + fname_anat
print '.. Centerline: ' + fname_centerline
print '.. Full width at half maximum: ' + str(fwhm)
print '.. Width of the square window: ' + str(width)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('cp '+fname_anat+' '+path_tmp+'data'+ext_anat, verbose)
sct.run('cp '+fname_centerline+' '+path_tmp+'centerline'+ext_centerline, verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
convert('data'+ext_anat, 'data.nii')
convert('centerline'+ext_centerline, 'centerline.nii')
# # Get dimensions of data
# sct.printv('\nGet dimensions of data...', param.verbose)
# nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
#
# #Delete existing tmp file in the current folder to avoid problems
# #Delete existing tmp file in the current folder to avoid problems
# if os.path.isfile('tmp.anat.nii'):
# sct.run('rm tmp.anat.nii')
# if os.path.isfile('tmp.centerline.nii'):
# sct.run('rm tmp.centerline.nii')
#
# # Convert to nii and delete nii.gz if still existing
# print '\nCopy input data...'
# sct.run('cp ' + fname_anat + ' tmp.anat'+ext_anat)
# convert('data'+ext_data, 'data.nii')
#
# sct.run('fslchfiletype NIFTI tmp.anat')
# if os.path.isfile('tmp.anat.nii.gz'):
# sct.run('rm tmp.anat.nii.gz')
# print '.. Anatomical image copied'
# sct.run('cp ' + fname_centerline + ' tmp.centerline'+ext_centerline)
# sct.run('fslchfiletype NIFTI tmp.centerline')
# if os.path.isfile('tmp.centerline.nii.gz'):
# sct.run('rm tmp.centerline.nii.gz')
# print '.. Centerline image copied'
# Open anatomical image
#==========================================================================================
# Reorient input anatomical volume into RL PA IS orientation
print '\nReorient input volume to RL PA IS orientation...'
sct.run(sct.fsloutput + 'fslswapdim tmp.anat RL PA IS tmp.anat_orient')
print '\nGet dimensions of input anatomical image...'
nx_a, ny_a, nz_a, nt_a, px_a, py_a, pz_a, pt_a = sct.get_dimension('tmp.anat_orient')
#nx_a, ny_a, nz_a, nt_a, px_a, py_a, pz_a, pt_a = sct.get_dimension(fname_anat)
print '.. matrix size: ' + str(nx_a) + ' x ' + str(ny_a) + ' x ' + str(nz_a)
print '.. voxel size: ' + str(px_a) + 'mm x ' + str(py_a) + 'mm x ' + str(pz_a) + 'mm'
print '\nOpen anatomical volume...'
file = nibabel.load('tmp.anat_orient.nii')
#file = nibabel.load(fname_anat)
data_anat = file.get_data()
data_anat=np.array(data_anat)
data_anat_smoothed=np.copy(data_anat)
# Open centerline
#==========================================================================================
# Reorient binary point into RL PA IS orientation
print '\nReorient centerline volume into RL PA IS orientation...'
sct.run(sct.fsloutput + 'fslswapdim tmp.centerline RL PA IS tmp.centerline_orient')
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('tmp.centerline_orient')
#nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_centerline)
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) + 'mm'
print '\nOpen centerline volume...'
file = nibabel.load('tmp.centerline_orient.nii')
#file = nibabel.load(fname_centerline)
data_centerline = file.get_data()
#Loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(0, nz, 1)]
y_centerline = [0 for iz in range(0, nz, 1)]
z_centerline = [iz for iz in range(0, nz, 1)]
for iz in range(0, nz, 1):
x_centerline[iz], y_centerline[iz] = np.unravel_index(data_centerline[:, :, iz].argmax(),
data_centerline[:, :, iz].shape)
del data_centerline
# Fit polynomial function through centerline
#==========================================================================================
#Fit centerline in the Z-X plane using polynomial function
print '\nFit centerline in the Z-X plane using polynomial function...'
coeffsx = np.polyfit(z_centerline, x_centerline, deg=param.deg_poly)
polyx = np.poly1d(coeffsx)
x_centerline_fit = np.polyval(polyx, z_centerline)
#Fit centerline in the Z-Y plane using polynomial function
print '\nFit centerline in the Z-Y plane using polynomial function...'
coeffsy = np.polyfit(z_centerline, y_centerline, deg=param.deg_poly)
polyy = np.poly1d(coeffsy)
y_centerline_fit = np.polyval(polyy, z_centerline)
# Find tangent function of centerline along z
#==========================================================================================
# Find tangent to centerline in zx plane, along z
print '\nFind tangent to centerline along z, in the Z-X plane...'
poly_tangent_xz = np.polyder(polyx)
tangent_xz = np.polyval(poly_tangent_xz, z_centerline)
# Find tangent to centerline in zy plane, along z
print '\nFind tangent to centerline along z, in the Z-Y plane...'
poly_tangent_yz = np.polyder(polyy)
tangent_yz = np.polyval(poly_tangent_yz, z_centerline)
# Create a Gaussian kernel with users parameters
#==========================================================================================
print '\nGenerate a Gaussian kernel with users parameters... '
# Convert the fwhm given by users in standard deviation (sigma) and find the size of gaussian kernel knowing
# that size_kernel=(6*sigma-1) must be odd
sigma = int(np.round((fwhm/pz_a)*(math.sqrt(1/(2*(math.log(2)))))))
size_kernel= (np.round(6*sigma))
if size_kernel%2==0:
size_kernel=size_kernel-1
#Creates an 1D-array impulsion and apply a gaussian filter. The result is a Gaussian kernel.
kernel_temp = np.zeros(size_kernel)
kernel_temp[math.ceil(size_kernel/2)] = 1
kernel= ndimage.filters.gaussian_filter1d(kernel_temp, sigma, order=0)
sum_kernel=np.sum(kernel)
print '.. Full width at half maximum: ' + str(fwhm)
print '.. Kernel size : '+str(size_kernel)
print '.. Sigma (Standard deviation): ' + str(sigma)
del kernel_temp
## Smooth along the spinal cord
##==========================================================================================
print '\nSmooth along the spinal cord...'
print '\n Voxel position along z axis...'
# Initialisations
position=np.zeros(3)
flag=np.zeros((nx_a,ny_a,nz_a))
data_weight=np.ones((nx_a,ny_a,nz_a))
smoothing_array=np.zeros(size_kernel)
x_near=np.zeros(2)
y_near=np.zeros(2)
z_near=np.zeros(2)
floor_position=np.zeros(3)
ceil_position=np.zeros(3)
position_d=np.zeros(3)
#For every voxel along z axis,
for iz in range(0,nz_a,1):
print '.. '+str(iz+1)+ '/'+str(nz_a)
# Determine the square area to smooth around the centerline
xmin=x_centerline[iz]-int(width/2)
xmax=x_centerline[iz]+int(width/2)
ymin=y_centerline[iz]-int(width/2)
ymax=y_centerline[iz]+int(width/2)
#Find the angle between the tangent and the x axis in xz plane.
theta_xz = -(math.atan(tangent_xz[iz]))
#Find the angle between the tangent and the y axis in yz plane.
theta_yz = -(math.atan(tangent_yz[iz]))
#Construct a rotation array around y axis.
Rxz=np.zeros((3,3))
Rxz[1,1]=1
Rxz[0,0]=(math.cos(theta_xz))
Rxz[2,0]=(math.sin(theta_xz))
Rxz[0,2]=-(math.sin(theta_xz))
Rxz[2,2]=(math.cos(theta_xz))
#Construct a rotation array around x axis.
Ryz=np.zeros((3,3))
Ryz[0,0]=1
Ryz[1,1]=(math.cos(theta_yz))
Ryz[1,2]=(math.sin(theta_yz))
Ryz[2,1]=-(math.sin(theta_yz))
Ryz[2,2]=(math.cos(theta_yz))
#For every voxels in the given plane, included in the square area
for ix in range(xmin,xmax,1):
for iy in range(ymin,ymax,1):
#The area to smooth has the same high as the 1D mask length
isize=0
centerline_point=[np.copy(x_centerline[iz]), np.copy(y_centerline[iz]), np.copy(iz)]
#For every voxels along the line orthogonal to the considered plane and included in the kernel.
#(Here we full a vector called smoothing_array, which has the same length as the kernel, is oriented in the direction of centerline and contains interpolated values of intensity)
for isize in range(0,size_kernel, 1):
#Find the position in the xy plane, before rotation
position = [ix, iy, iz+isize-(np.floor(size_kernel/2))]
#Find the position after rotation by multiplying the position centered on centerline point with rotation array around x and y axis.
new_position= np.dot((np.dot((np.subtract(np.copy(position),centerline_point)), Rxz)), Ryz) + centerline_point
#If the resulting voxel is out of image boundaries, pad the smoothing array with a zero
if (new_position[0]<0)or (new_position[1]<0)or(new_position[2]<0)or(new_position[0]>nx_a-1)or (new_position[1]>ny_a-1)or(new_position[2]>nz_a-1):
smoothing_array[isize]=0
#Otherwise, fill the smoothing array with the linear interpolation of values around the actual position
else:
# Trilinear interpolation
#==========================================================================================================================================
# Determine the coordinates in grid surrounding the position of the central voxel and perform a trilinear interpolation
x_near[0]=np.copy(np.floor(new_position[0]))
x_near[1]=np.copy(np.ceil(new_position[0]))
xd=(new_position[0]-x_near[0])
y_near[0]=np.copy(np.floor(new_position[1]))
y_near[1]=np.copy(np.ceil(new_position[1]))
yd=(new_position[1]-y_near[0])
z_near[0]=np.copy(np.floor(new_position[2]))
z_near[1]=np.copy(np.ceil(new_position[2]))
zd=(new_position[2]-z_near[0])
c00=((data_anat[x_near[0],y_near[0],z_near[0]])*(1-xd))+((data_anat[x_near[1],y_near[0],z_near[0]])*(xd))
c10=((data_anat[x_near[0],y_near[1],z_near[0]])*(1-xd))+((data_anat[x_near[1],y_near[1],z_near[0]])*(xd))
c01=((data_anat[x_near[0],y_near[0],z_near[1]])*(1-xd))+((data_anat[x_near[1],y_near[0],z_near[1]])*(xd))
c11=((data_anat[x_near[0],y_near[1],z_near[1]])*(1-xd))+((data_anat[x_near[1],y_near[1],z_near[1]])*(xd))
c0=c00*(1-yd)+c10*yd
c1=c01*(1-yd)+c11*yd
smoothing_array[isize]=c0*(1-zd)+c1*zd
#If actual position is in the z=z_centerline plane, save the coordinates in the variable central_position. (Otherwise, don't save it).
if isize==(np.floor(size_kernel/2)):
central_position=np.copy(new_position)
#If the central_position is out of boundaries, don't consider it anymore.
if (central_position[0]<0)or (central_position[1]<0)or(central_position[2]<0)or(central_position[0]>nx_a-1)or (central_position[1]>ny_a-1)or(central_position[2]>nz_a-1):
continue
else:
#Otherwise, perform the convolution of the smoothing_array and the kernel for the central voxel only (equivalent to element-wise multiply). Normalize the result.
result=((np.sum(np.copy(smoothing_array)*kernel))/sum_kernel)
# Determine the coordinates in grid surrounding the position of the central voxel
for i in range(0,3,1):
floor_position[i]=math.floor(central_position[i])
ceil_position[i]=math.ceil(central_position[i])
position_d[i]=central_position[i]-floor_position[i]
# Reverse trilinear interpolation
#==========================================================================================================================================
# Split the resuling intensity given by the convolution between the 8 voxels surrounding the point where the convolution is calculated (central_position).
# The array data_anat_smoothed is the the volume os the anatomical image smoothed alog the spinal cord.
# The array flag is a volume that indicates if a the corresponding voxel in the anatomical image is inside the smoothing area around the spinal cord and if there is already been an operation on this voxel.
# The default value of flag is 0. If it is set to 1, it means there is an operation on the corresponding voxel in anatomical image. Then we clear both the data_anat_smoothed and data_weight corresponding voxel to 0.
# The array data_weight represent the is represent the sum of weights used to calculate the intensity for every voxel. In a perfect case, this sum would be 1, but because there is an angle between
# two adjacent planes, the sum will be lower so we need to normalize the result. The default value for data_weight is 1, but once there is an operation on the corresponding voxel (flag=1), we accumulate the weights used.
if (flag[ceil_position[0],ceil_position[1],ceil_position[2]]==0):
data_anat_smoothed[ceil_position[0],ceil_position[1],ceil_position[2]]=0
data_weight[ceil_position[0],ceil_position[1],ceil_position[2]]=0
flag[ceil_position[0],ceil_position[1],ceil_position[2]]=1
weight=(position_d[0])*(position_d[1])*(position_d[2])
data_anat_smoothed[ceil_position[0],ceil_position[1],ceil_position[2]]=data_anat_smoothed[ceil_position[0],ceil_position[1],ceil_position[2]]+(weight*result)
data_weight[ceil_position[0],ceil_position[1],ceil_position[2]]=data_weight[ceil_position[0],ceil_position[1],ceil_position[2]]+(weight)
if (flag[floor_position[0],floor_position[1],floor_position[2]]==0):
data_anat_smoothed[floor_position[0],floor_position[1],floor_position[2]]=0
data_weight[floor_position[0],floor_position[1],floor_position[2]]=0
flag[floor_position[0],floor_position[1],floor_position[2]]=1
weight=(1-position_d[0])*(1-position_d[1])*(1-position_d[2])
data_anat_smoothed[floor_position[0],floor_position[1],floor_position[2]]=data_anat_smoothed[floor_position[0],floor_position[1],floor_position[2]]+(weight*result)
data_weight[floor_position[0],floor_position[1],floor_position[2]]=data_weight[floor_position[0],floor_position[1],floor_position[2]]+(weight)
if (flag[ceil_position[0],floor_position[1],floor_position[2]]==0):
data_anat_smoothed[ceil_position[0],floor_position[1],floor_position[2]]=0
data_weight[ceil_position[0],floor_position[1],floor_position[2]]=0
flag[ceil_position[0],floor_position[1],floor_position[2]]=1
weight=(position_d[0])*(1-position_d[1])*(1-position_d[2])
data_anat_smoothed[ceil_position[0],floor_position[1],floor_position[2]]=data_anat_smoothed[ceil_position[0],floor_position[1],floor_position[2]]+(weight*result)
data_weight[ceil_position[0],floor_position[1],floor_position[2]]=data_weight[ceil_position[0],floor_position[1],floor_position[2]]+(weight)
if (flag[ceil_position[0],ceil_position[1],floor_position[2]]==0):
data_anat_smoothed[ceil_position[0],ceil_position[1],floor_position[2]]=0
data_weight[ceil_position[0],ceil_position[1],floor_position[2]]=0
flag[ceil_position[0],ceil_position[1],floor_position[2]]=1
weight=(position_d[0])*(position_d[1])*(1-position_d[2])
data_anat_smoothed[ceil_position[0],ceil_position[1],floor_position[2]]=data_anat_smoothed[ceil_position[0],ceil_position[1],floor_position[2]]+(weight*result)
data_weight[ceil_position[0],ceil_position[1],floor_position[2]]=data_weight[ceil_position[0],ceil_position[1],floor_position[2]]+(weight)
if (flag[ceil_position[0],floor_position[1],ceil_position[2]]==0):
data_anat_smoothed[ceil_position[0],floor_position[1],ceil_position[2]]=0
data_weight[ceil_position[0],floor_position[1],ceil_position[2]]=0
flag[ceil_position[0],floor_position[1],ceil_position[2]]=1
weight=(position_d[0])*(1-position_d[1])*(position_d[2])
data_anat_smoothed[ceil_position[0],floor_position[1],ceil_position[2]]=data_anat_smoothed[ceil_position[0],floor_position[1],ceil_position[2]]+(weight*result)
data_weight[ceil_position[0],floor_position[1],ceil_position[2]]=data_weight[ceil_position[0],floor_position[1],ceil_position[2]]+(weight)
if (flag[floor_position[0],ceil_position[1],floor_position[2]]==0):
data_anat_smoothed[floor_position[0],ceil_position[1],floor_position[2]]=0
data_weight[floor_position[0],ceil_position[1],floor_position[2]]=0
flag[floor_position[0],ceil_position[1],floor_position[2]]=1
weight=(1-position_d[0])*(position_d[1])*(1-position_d[2])
data_anat_smoothed[floor_position[0],ceil_position[1],floor_position[2]]=data_anat_smoothed[floor_position[0],ceil_position[1],floor_position[2]]+(weight*result)
data_weight[floor_position[0],ceil_position[1],floor_position[2]]=data_weight[floor_position[0],ceil_position[1],floor_position[2]]+(weight)
if (flag[floor_position[0],ceil_position[1],ceil_position[2]]==0):
data_anat_smoothed[floor_position[0],ceil_position[1],ceil_position[2]]=0
data_weight[floor_position[0],ceil_position[1],ceil_position[2]]=0
flag[floor_position[0],ceil_position[1],ceil_position[2]]=1
weight=(1-position_d[0])*(position_d[1])*(position_d[2])
data_anat_smoothed[floor_position[0],ceil_position[1], ceil_position[2]]= data_anat_smoothed[floor_position[0],ceil_position[1], ceil_position[2]]+(weight*result)
data_weight[floor_position[0],ceil_position[1], ceil_position[2]]= data_weight[floor_position[0],ceil_position[1], ceil_position[2]]+(weight)
if (flag[floor_position[0],floor_position[1],ceil_position[2]]==0):
data_anat_smoothed[floor_position[0],floor_position[1],ceil_position[2]]=0
flag[floor_position[0],floor_position[1],ceil_position[2]]=1
data_weight[floor_position[0],floor_position[1],ceil_position[2]]=0
weight=(1-position_d[0])*(1-position_d[1])*(position_d[2])
data_anat_smoothed[floor_position[0],floor_position[1],ceil_position[2]]=data_anat_smoothed[floor_position[0],floor_position[1],ceil_position[2]]+(weight*result)
data_weight[floor_position[0],floor_position[1],ceil_position[2]]=data_weight[floor_position[0],floor_position[1],ceil_position[2]]+(weight)
# Once we covered the whole spinal cord along z, we normalize the resulting image considering the weight used to calculate each voxel intensity
data_anat_smoothed=data_anat_smoothed/data_weight
#Generate output file
#==========================================================================================
# Write NIFTI volumes
print '\nWrite NIFTI volumes...'
if os.path.isfile('tmp.im_smoothed.nii'):
sct.run('rm tmp.im_smoothed.nii')
img = nibabel.Nifti1Image(data_anat_smoothed, None)
nibabel.save(img, 'tmp.im_smoothed.nii')
print '.. File created: tmp.im_smoothed.nii'
#Copy header geometry from input data
print '\nCopy header geometry from input data and reorient the volume...'
sct.run(sct.fsloutput+'fslcpgeom tmp.anat_orient.nii tmp.im_smoothed.nii ')
#Generate output file
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.im_smoothed.nii','./',file_anat+'_smoothed',ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm tmp.anat.nii')
sct.run('rm tmp.centerline.nii')
sct.run('rm tmp.anat_orient.nii')
sct.run('rm tmp.centerline_orient.nii')
#Display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished!'
print '.. '+str(int(round(elapsed_time)))+'s\n'
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
centerline_fitting = 'polynome'
remove_temp_files = param.remove_temp_files
interp = param.interp
degree_poly = param.deg_poly
# extract path of the script
path_script = os.path.dirname(__file__)+'/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data+'/t2/t2.nii.gz'
fname_centerline = path_sct_data+'/t2/t2_seg.nii.gz'
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:c:r:d:f:s:')
except getopt.GetoptError as err:
print str(err)
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-d'):
degree_poly = int(arg)
elif opt in ('-f'):
centerline_fitting = str(arg)
elif opt in ('-s'):
interp = str(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... '+fname_anat
print ' Centerline ........................ '+fname_centerline
print ''
# Get input image orientation
input_image_orientation = get_orientation(fname_anat)
# Reorient input data into RL PA IS orientation
set_orientation(fname_anat, 'RPI', 'tmp.anat_orient.nii')
set_orientation(fname_centerline, 'RPI', 'tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('tmp.centerline_orient.nii')
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
print '\nOpen centerline volume...'
file = nibabel.load('tmp.centerline_orient.nii')
data = file.get_data()
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index+1, 1):
x_centerline[iz-min_z_index], y_centerline[iz-min_z_index] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index+1, 1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
if len(x_seg) > 0:
x_centerline[iz-min_z_index] = numpy.mean(x_seg)
y_centerline[iz-min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'splines':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(x_centerline,y_centerline,z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
sct.run(sct.fsloutput + 'fslsplit tmp.anat_orient.nii tmp.anat_z -z')
file_anat_split = ['tmp.anat_z'+str(z).zfill(4) for z in range(0,nz,1)]
# initialize variables
file_mat_inv_cumul = ['tmp.mat_inv_cumul_z'+str(z).zfill(4) for z in range(0,nz,1)]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[max_z_index-min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(min_z_index, max_z_index+1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz-min_z_index] == 0 and y_centerline[iz-min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[iz-min_z_index]
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, 0) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
for iz in range(max_z_index+1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement_max_z_index) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = ['tmp.anat_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_anat_split_fit[iz]+' -interp '+interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
set_orientation('tmp.anat_orient_fit.nii', input_image_orientation, 'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii', file_anat+'_flatten'+ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview '+file_anat+ext_anat+' '+file_anat+'_flatten'+ext_anat+' &\n'
def main():
# Initialization
fname_anat = ''
fname_landmark_anat = ''
fname_template = ''
fname_landmark_template = ''
fname_mask = ''
remove_temp_files = param.remove_temp_files
number_iterations = param.number_iterations
verbose = param.verbose
start_time = time.time()
# extract path of the script
path_script = os.path.dirname(__file__)+'/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_anat = path_script+'../testing/sct_register_straight_spinalcord_to_template/data/errsm_22_t2_cropped_rpi_straight.nii.gz'
fname_landmark_anat = path_script+'../testing/sct_register_straight_spinalcord_to_template/data/landmarks_C2_T5.nii.gz'
fname_seg_anat = path_script+'../testing/sct_register_straight_spinalcord_to_template/data/landmarks_C2_T5.nii.gz'
fname_template = path_script+'../data/template/MNI-Poly-AMU_T2.nii.gz'
fname_landmark_template = path_script+'../data/template/landmarks_C2_T5.nii.gz'
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:f:l:m:n:o:r:s:t:v:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-f'):
fname_landmark_template = arg
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-l'):
fname_landmark_anat = arg
elif opt in ('-m'):
fname_mask = arg
elif opt in ('-n'):
number_iterations = arg
elif opt in ("-o"):
fname_template_seg = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ("-s"):
fname_anat_seg = arg
elif opt in ('-t'):
fname_template = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_landmark_anat == '' or fname_template == '' or fname_landmark_template == '':
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_landmark_anat)
sct.check_file_exist(fname_template)
sct.check_file_exist(fname_landmark_template)
sct.check_file_exist(fname_seg_template)
# Display arguments
print '\nCheck input arguments:'
print ' straight anatomic: '+fname_anat
print ' landmarks anatomic: '+fname_landmark_anat
print ' template T2: '+fname_template
print ' template landmarks: '+fname_landmark_template
print ' template segmentation:'+fname_landmark_template
print ' number of iterations: '+str(number_iterations)
print ' mask anatomic: '+fname_mask
print ' Verbose: '+str(verbose)
# Get full path
fname_anat = os.path.abspath(fname_anat)
fname_landmark_anat = os.path.abspath(fname_landmark_anat)
fname_template = os.path.abspath(fname_template)
fname_landmark_template = os.path.abspath(fname_landmark_template)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_template, file_template, ext_template = sct.extract_fname(fname_template)
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Estimate transfo: straight --> template (affine landmark-based)'
print '\nEstimate transfo: straight anat --> template (affine landmark-based)...'
sct.run('ANTSUseLandmarkImagesToGetAffineTransform '+fname_landmark_template+' '+fname_landmark_anat+' affine tmp.straight2templateAffine.txt')
# Apply transformation: straight --> template
print '\nApply transformation straight --> template...'
sct.run('WarpImageMultiTransform 3 '+fname_anat+' tmp.straight2templateAffine.nii tmp.straight2templateAffine.txt -R '+fname_template)
# Estimate transformation: straight --> template (deformation)
print '\nEstimate transformation: straight --> template (diffeomorphic transformation). Takes ~15-45 minutes...'
cmd = 'antsRegistration \
--dimensionality 3 \
--transform SyN[0.2,3] \
--metric MI['+fname_template+',tmp.straight2templateAffine.nii,1,32] \
--convergence '+number_iterations+' \
--shrink-factors 4x1 \
--smoothing-sigmas 1x0mm \
--Restrict-Deformation 1x1x0 \
--output [tmp.straight2template,tmp.straight2template.nii.gz] \
--collapse-output-transforms 1 \
--interpolation BSpline[3] \
--winsorize-image-intensities [0.005,0.995]'
if fname_mask != '':
# TODO: check if mask exist
cmd = cmd+' -x '+fname_mask
# run command
status, output = sct.run(cmd)
if verbose:
print output
# Concatenate affine and non-linear transformations...
print '\nConcatenate affine and non-linear transformations: straight --> template...'
# NB: cannot use sct.run() because output of ComposeMultiTransform is not 0, even if there is no error (bug in ANTS-- already reported on 2013-12-30)
cmd = 'ComposeMultiTransform 3 tmp.warp_straight2template.nii.gz -R '+fname_template+' tmp.straight2template0Warp.nii.gz tmp.straight2templateAffine.txt'
print('>> '+cmd)
commands.getstatusoutput(cmd)
# Concatenate affine and non-linear transformations...
print '\nConcatenate affine and non-linear transformations: template --> straight...'
# NB: cannot use sct.run() because output of ComposeMultiTransform is not 0, even if there is no error (bug in ANTS-- already reported on 2013-12-30)
cmd = 'ComposeMultiTransform 3 tmp.warp_template2straight.nii.gz -R '+fname_anat+' -i tmp.straight2templateAffine.txt tmp.straight2template0InverseWarp.nii.gz'
print('>> '+cmd)
commands.getstatusoutput(cmd)
# Apply transformation: template --> straight
print '\nApply transformation: template --> straight...'
sct.run('WarpImageMultiTransform 3 '+fname_template+' tmp.template2straight.nii.gz'+' -R '+fname_anat+' tmp.warp_template2straight.nii.gz')
# THIS CODE USES 2-STEP METHOD WITH SEGMENTATION
# # Estimate transfo: straight --> template (affine landmark-based)'
# print '\nEstimate transfo: straight anat --> template (affine landmark-based)...'
# sct.run('ANTSUseLandmarkImagesToGetAffineTransform '+fname_landmark_template+' '+fname_landmark_anat+' affine tmp.straight2templateAffine.txt')
#
# # Apply transformation: straight --> template
# print '\nApply transformation straight --> template...'
# sct.run('WarpImageMultiTransform 3 '+fname_anat+' tmp.straight2templateAffine.nii tmp.straight2templateAffine.txt -R '+fname_template)
# sct.run('WarpImageMultiTransform 3 '+fname_anat_seg+' tmp.straightSeg2templateAffine.nii tmp.straight2templateAffine.txt -R '+fname_template)
#
# # Estimate transformation using ANTS
# print('\nStep #1: Estimate transformation using spinal cord segmentations...')
#
# cmd = 'antsRegistration \
# --dimensionality 3 \
# --transform SyN[0.2,3,0] \
# --metric MI['+fname_template_seg+',tmp.straightSeg2templateAffine.nii,1,32] \
# --convergence 50x10 \
# --shrink-factors 2x1 \
# --smoothing-sigmas 2x1mm \
# --Restrict-Deformation 1x1x0 \
# --output [tmp.regSeg,tmp.straightSeg2template.nii.gz]'
#
# # run command
# status, output = sct.run(cmd)
# if verbose:
# print output
#
# # Apply warping field: seg --> template_seg
# print '\nApply transformation anat_seg --> template_seg...'
# sct.run('WarpImageMultiTransform 3 '+fname_anat+' tmp.straight2templateStep1.nii tmp.regSeg0Warp.nii.gz -R '+fname_template)
#
# print('\nStep #2: Improve local deformation using images (start from previous transformation)...')
#
# # Estimate transformation: straight --> template (deformation)
# print '\nEstimate transformation: straight --> template (diffeomorphic transformation). Takes 10-45 minutes...'
# cmd = 'antsRegistration \
# --dimensionality 3 \
# --transform SyN[0.1,1,0] \
# --metric CC['+fname_template+',tmp.straight2templateStep1.nii,1,4] \
# --convergence 20 \
# --shrink-factors 1 \
# --smoothing-sigmas 0mm \
# --Restrict-Deformation 1x1x0 \
# --output [tmp.straight2template,tmp.straight2template.nii.gz] \
# --interpolation BSpline[3]'
#
# # use mask (if provided by user)
# if fname_mask != '':
# # TODO: check if mask exist
# cmd = cmd+' -x '+fname_mask
#
# # run command
# status, output = sct.run(cmd)
# if verbose:
# print output
#
# # Concatenate affine and non-linear transformations...
# print '\nConcatenate affine and non-linear transformations: straight --> template...'
# # NB: cannot use sct.run() because output of ComposeMultiTransform is not 0, even if there is no error (bug in ANTS-- already reported on 2013-12-30)
# cmd = 'ComposeMultiTransform 3 tmp.warp_straight2template.nii.gz -R '+fname_template+' tmp.straight2template0Warp.nii.gz tmp.regSeg0Warp.nii.gz tmp.straight2templateAffine.txt'
# print('>> '+cmd)
# commands.getstatusoutput(cmd)
#
# # Concatenate affine and non-linear transformations...
# print '\nConcatenate affine and non-linear transformations: template --> straight...'
# # NB: cannot use sct.run() because output of ComposeMultiTransform is not 0, even if there is no error (bug in ANTS-- already reported on 2013-12-30)
# cmd = 'ComposeMultiTransform 3 tmp.warp_template2straight.nii.gz -R '+fname_anat+' -i tmp.straight2templateAffine.txt tmp.straight2template0InverseWarp.nii.gz'
# print('>> '+cmd)
# commands.getstatusoutput(cmd)
#
# # Apply transformation: template --> straight
# print '\nApply transformation: template --> straight...'
# sct.run('WarpImageMultiTransform 3 '+fname_template+' tmp.template2straight.nii.gz'+' -R '+fname_anat+' tmp.warp_template2straight.nii.gz')
#
# Generate output file (in current folder)
print '\nGenerate output file...'
sct.generate_output_file('tmp.warp_template2straight.nii.gz','./','warp_template2straight',ext_anat) # warping field template --> straight
sct.generate_output_file('tmp.warp_straight2template.nii.gz','./','warp_straight2template',ext_anat) # warping field straight --> template
sct.generate_output_file('tmp.straight2template.nii.gz','./',file_anat+'2template',ext_anat) # anat --> template
sct.generate_output_file('tmp.template2straight.nii.gz','./',file_template+'2straight',ext_anat) # anat --> template
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm tmp.*')
elapsed_time = time.time() - start_time
print '\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s\n'
def main():
# get path of the toolbox
status, path_sct = getstatusoutput('echo $SCT_DIR')
#print path_sct
#Initialization
fname = ''
landmark = ''
verbose = param.verbose
output_name = 'aligned.nii.gz'
template_landmark = ''
final_warp = param.final_warp
compose = param.compose
transfo = 'affine'
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:l:o:R:t:w:c:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-l"):
landmark = arg
elif opt in ("-o"):
output_name = arg
elif opt in ("-R"):
template_landmark = arg
elif opt in ("-t"):
transfo = arg
elif opt in ("-w"):
final_warp = arg
elif opt in ("-c"):
compose = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname == '' or landmark == '' or template_landmark == '' :
usage()
if final_warp not in ['','spline','NN']:
usage()
if transfo not in ['affine', 'bspline', 'SyN', 'nurbs']:
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
sct.check_file_exist(landmark)
sct.check_file_exist(template_landmark)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Verbose ........................... '+str(verbose)
if transfo == 'affine':
print 'Creating cross using input landmarks\n...'
sct.run('sct_label_utils -i ' + landmark + ' -o ' + 'cross_native.nii.gz -t cross ' )
print 'Creating cross using template landmarks\n...'
sct.run('sct_label_utils -i ' + template_landmark + ' -o ' + 'cross_template.nii.gz -t cross ' )
print 'Computing affine transformation between subject and destination landmarks\n...'
os.system('isct_ANTSUseLandmarkImagesToGetAffineTransform cross_template.nii.gz cross_native.nii.gz affine n2t.txt')
warping = 'n2t.txt'
elif transfo == 'nurbs':
warping_subject2template = 'warp_subject2template.nii.gz'
warping_template2subject = 'warp_template2subject.nii.gz'
tmp_name = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + tmp_name)
tmp_abs_path = os.path.abspath(tmp_name)
sct.run('cp ' + landmark + ' ' + tmp_abs_path)
os.chdir(tmp_name)
from msct_image import Image
image_landmark = Image(landmark)
image_template = Image(template_landmark)
landmarks_input = image_landmark.getNonZeroCoordinates(sorting='value')
landmarks_template = image_template.getNonZeroCoordinates(sorting='value')
min_value = min([int(landmarks_input[0].value), int(landmarks_template[0].value)])
max_value = max([int(landmarks_input[-1].value), int(landmarks_template[-1].value)])
nx, ny, nz, nt, px, py, pz, pt = image_landmark.dim
displacement_subject2template, displacement_template2subject = [], []
for value in range(min_value, max_value+1):
is_in_input = False
coord_input = None
for coord in landmarks_input:
if int(value) == int(coord.value):
coord_input = coord
is_in_input = True
break
is_in_template = False
coord_template = None
for coord in landmarks_template:
if int(value) == int(coord.value):
coord_template = coord
is_in_template = True
break
if is_in_template and is_in_input:
displacement_subject2template.append([0.0, coord_input.z, coord_template.z - coord_input.z])
displacement_template2subject.append([0.0, coord_template.z, coord_input.z - coord_template.z])
# create displacement field
from numpy import zeros
from nibabel import Nifti1Image, save
data_warp_subject2template = zeros((nx, ny, nz, 1, 3))
data_warp_template2subject = zeros((nx, ny, nz, 1, 3))
hdr_warp = image_template.hdr.copy()
hdr_warp.set_intent('vector', (), '')
hdr_warp.set_data_dtype('float32')
# approximate displacement with nurbs
from msct_smooth import b_spline_nurbs
displacement_z = [item[1] for item in displacement_subject2template]
displacement_x = [item[2] for item in displacement_subject2template]
verbose = 1
displacement_z, displacement_y, displacement_y_deriv, displacement_z_deriv = b_spline_nurbs(displacement_x, displacement_z, None, nbControl=None, verbose=verbose, all_slices=True)
arg_min_z, arg_max_z = np.argmin(displacement_y), np.argmax(displacement_y)
min_z, max_z = int(displacement_y[arg_min_z]), int(displacement_y[arg_max_z])
displac = []
for index, iz in enumerate(displacement_y):
displac.append([iz, displacement_z[index]])
for iz in range(0, min_z):
displac.append([iz, displacement_z[arg_min_z]])
for iz in range(max_z, nz):
displac.append([iz, displacement_z[arg_max_z]])
for item in displac:
if 0 <= item[0] < nz:
data_warp_template2subject[:, :, item[0], 0, 2] = item[1] * pz
displacement_z = [item[1] for item in displacement_template2subject]
displacement_x = [item[2] for item in displacement_template2subject]
verbose = 1
displacement_z, displacement_y, displacement_y_deriv, displacement_z_deriv = b_spline_nurbs(displacement_x, displacement_z, None, nbControl=None, verbose=verbose, all_slices=True)
arg_min_z, arg_max_z = np.argmin(displacement_y), np.argmax(displacement_y)
min_z, max_z = int(displacement_y[arg_min_z]), int(displacement_y[arg_max_z])
displac = []
for index, iz in enumerate(displacement_y):
displac.append([iz, displacement_z[index]])
for iz in range(0, min_z):
displac.append([iz, displacement_z[arg_min_z]])
for iz in range(max_z, nz):
displac.append([iz, displacement_z[arg_max_z]])
for item in displac:
data_warp_subject2template[:, :, item[0], 0, 2] = item[1] * pz
img = Nifti1Image(data_warp_template2subject, None, hdr_warp)
save(img, warping_template2subject)
sct.printv('\nDONE ! Warping field generated: ' + warping_template2subject, verbose)
img = Nifti1Image(data_warp_subject2template, None, hdr_warp)
save(img, warping_subject2template)
sct.printv('\nDONE ! Warping field generated: ' + warping_subject2template, verbose)
# Copy warping into parent folder
sct.run('cp ' + warping_subject2template + ' ../' + warping_subject2template)
sct.run('cp ' + warping_template2subject + ' ../' + warping_template2subject)
warping = warping_subject2template
os.chdir('..')
remove_temp_files = True
if remove_temp_files:
sct.run('rm -rf ' + tmp_name)
elif transfo == 'SyN':
warping = 'warp_subject2template.nii.gz'
tmp_name = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+tmp_name)
tmp_abs_path = os.path.abspath(tmp_name)
sct.run('cp ' + landmark + ' ' + tmp_abs_path)
os.chdir(tmp_name)
# sct.run('sct_label_utils -i '+landmark+' -t dist-inter')
# sct.run('sct_label_utils -i '+template_landmark+' -t plan -o template_landmarks_plan.nii.gz -c 5')
# sct.run('sct_crop_image -i template_landmarks_plan.nii.gz -o template_landmarks_plan_cropped.nii.gz -start 0.35,0.35 -end 0.65,0.65 -dim 0,1')
# sct.run('sct_label_utils -i '+landmark+' -t plan -o landmarks_plan.nii.gz -c 5')
# sct.run('sct_crop_image -i landmarks_plan.nii.gz -o landmarks_plan_cropped.nii.gz -start 0.35,0.35 -end 0.65,0.65 -dim 0,1')
# sct.run('isct_antsRegistration --dimensionality 3 --transform SyN[0.5,3,0] --metric MeanSquares[template_landmarks_plan_cropped.nii.gz,landmarks_plan_cropped.nii.gz,1] --convergence 400x200 --shrink-factors 4x2 --smoothing-sigmas 4x2mm --restrict-deformation 0x0x1 --output [landmarks_reg,landmarks_reg.nii.gz] --interpolation NearestNeighbor --float')
# sct.run('isct_c3d -mcs landmarks_reg0Warp.nii.gz -oo warp_vecx.nii.gz warp_vecy.nii.gz warp_vecz.nii.gz')
# sct.run('isct_c3d warp_vecz.nii.gz -resample 200% -o warp_vecz_r.nii.gz')
# sct.run('isct_c3d warp_vecz_r.nii.gz -smooth 0x0x3mm -o warp_vecz_r_sm.nii.gz')
# sct.run('sct_crop_image -i warp_vecz_r_sm.nii.gz -o warp_vecz_r_sm_line.nii.gz -start 0.5,0.5 -end 0.5,0.5 -dim 0,1 -b 0')
# sct.run('sct_label_utils -i warp_vecz_r_sm_line.nii.gz -t plan_ref -o warp_vecz_r_sm_line_extended.nii.gz -c 0 -r '+template_landmark)
# sct.run('isct_c3d '+template_landmark+' warp_vecx.nii.gz -reslice-identity -o warp_vecx_res.nii.gz')
# sct.run('isct_c3d '+template_landmark+' warp_vecy.nii.gz -reslice-identity -o warp_vecy_res.nii.gz')
# sct.run('isct_c3d warp_vecx_res.nii.gz warp_vecy_res.nii.gz warp_vecz_r_sm_line_extended.nii.gz -omc 3 '+warping) # no x?
#new
#put labels of the subject at the center of the image (for plan xOy)
import nibabel
from copy import copy
file_labels_input = nibabel.load(landmark)
hdr_labels_input = file_labels_input.get_header()
data_labels_input = file_labels_input.get_data()
data_labels_middle = copy(data_labels_input)
data_labels_middle *= 0
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(landmark).dim
X,Y,Z = data_labels_input.nonzero()
x_middle = int(round(nx/2.0))
y_middle = int(round(ny/2.0))
#put labels of the template at the center of the image (for plan xOy) #probably not necessary as already done by average labels
file_labels_template = nibabel.load(template_landmark)
hdr_labels_template = file_labels_template.get_header()
data_labels_template = file_labels_template.get_data()
data_template_middle = copy(data_labels_template)
data_template_middle *= 0
x, y, z = data_labels_template.nonzero()
max_num = min([len(z), len(Z)])
index_sort = np.argsort(Z)
index_sort = index_sort[::-1]
X = X[index_sort]
Y = Y[index_sort]
Z = Z[index_sort]
index_sort = np.argsort(z)
index_sort = index_sort[::-1]
x = x[index_sort]
y = y[index_sort]
z = z[index_sort]
for i in range(max_num):
data_labels_middle[x_middle, y_middle, Z[i]] = data_labels_input[X[i], Y[i], Z[i]]
img = nibabel.Nifti1Image(data_labels_middle, None, hdr_labels_input)
nibabel.save(img, 'labels_input_middle_xy.nii.gz')
for i in range(max_num):
data_template_middle[x_middle, y_middle, z[i]] = data_labels_template[x[i], y[i], z[i]]
img_template = nibabel.Nifti1Image(data_template_middle, None, hdr_labels_template)
nibabel.save(img_template, 'labels_template_middle_xy.nii.gz')
#estimate Bspline transform to register to template
sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField labels_template_middle_xy.nii.gz labels_input_middle_xy.nii.gz '+ warping+' 40x40x1 5 5 0')
# select centerline of warping field according to z and extend it
sct.run('isct_c3d -mcs '+warping+' -oo warp_vecx.nii.gz warp_vecy.nii.gz warp_vecz.nii.gz')
#sct.run('isct_c3d warp_vecz.nii.gz -resample 200% -o warp_vecz_r.nii.gz')
#sct.run('isct_c3d warp_vecz.nii.gz -smooth 0x0x3mm -o warp_vecz_r_sm.nii.gz')
sct.run('sct_crop_image -i warp_vecz.nii.gz -o warp_vecz_r_sm_line.nii.gz -start 0.5,0.5 -end 0.5,0.5 -dim 0,1 -b 0')
sct.run('sct_label_utils -i warp_vecz_r_sm_line.nii.gz -t plan_ref -o warp_vecz_r_sm_line_extended.nii.gz -r '+template_landmark)
sct.run('isct_c3d '+template_landmark+' warp_vecx.nii.gz -reslice-identity -o warp_vecx_res.nii.gz')
sct.run('isct_c3d '+template_landmark+' warp_vecy.nii.gz -reslice-identity -o warp_vecy_res.nii.gz')
sct.run('isct_c3d warp_vecx_res.nii.gz warp_vecy_res.nii.gz warp_vecz_r_sm_line_extended.nii.gz -omc 3 '+warping)
# check results
#dilate first labels
sct.run('fslmaths labels_input_middle_xy.nii.gz -dilF landmark_dilated.nii.gz') #new
sct.run('sct_apply_transfo -i landmark_dilated.nii.gz -o label_moved.nii.gz -d labels_template_middle_xy.nii.gz -w '+warping+' -x nn')
#undilate
sct.run('sct_label_utils -i label_moved.nii.gz -t cubic-to-point -o label_moved_2point.nii.gz')
sct.run('sct_label_utils -i labels_template_middle_xy.nii.gz -r label_moved_2point.nii.gz -o template_removed.nii.gz -t remove')
#end new
# check results
#dilate first labels
#sct.run('fslmaths '+landmark+' -dilF landmark_dilated.nii.gz') #old
#sct.run('sct_apply_transfo -i landmark_dilated.nii.gz -o label_moved.nii.gz -d '+template_landmark+' -w '+warping+' -x nn') #old
#undilate
#sct.run('sct_label_utils -i label_moved.nii.gz -t cubic-to-point -o label_moved_2point.nii.gz') #old
#sct.run('sct_label_utils -i '+template_landmark+' -r label_moved_2point.nii.gz -o template_removed.nii.gz -t remove') #old
# # sct.run('sct_apply_transfo -i '+landmark+' -o label_moved.nii.gz -d '+template_landmark+' -w '+warping+' -x nn')
# # sct.run('sct_label_utils -i '+template_landmark+' -r label_moved.nii.gz -o template_removed.nii.gz -t remove')
# # status, output = sct.run('sct_label_utils -i label_moved.nii.gz -r template_removed.nii.gz -t MSE')
status, output = sct.run('sct_label_utils -i label_moved_2point.nii.gz -r template_removed.nii.gz -t MSE')
sct.printv(output,1,'info')
remove_temp_files = False
if os.path.isfile('error_log_label_moved.txt'):
remove_temp_files = False
with open('log.txt', 'a') as log_file:
log_file.write('Error for '+fname+'\n')
# Copy warping into parent folder
sct.run('cp '+ warping+' ../'+warping)
os.chdir('..')
if remove_temp_files:
sct.run('rm -rf '+tmp_name)
# if transfo == 'bspline' :
# print 'Computing bspline transformation between subject and destination landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField cross_template.nii.gz cross_native.nii.gz warp_ntotemp.nii.gz 5x5x5 3 2 0')
# warping = 'warp_ntotemp.nii.gz'
# if final_warp == '' :
# print 'Apply transfo to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping + ' -n Linear')
# if final_warp == 'NN':
# print 'Apply transfo to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping + ' -n NearestNeighbor')
if final_warp == 'spline':
print 'Apply transfo to input image\n...'
sct.run('sct_apply_transfo -i ' + fname + ' -o ' + output_name + ' -d ' + template_landmark + ' -w ' + warping + ' -x spline')
# Remove warping
#os.remove(warping)
# if compose :
# print 'Computing affine transformation between subject and destination landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetAffineTransform cross_template.nii.gz cross_native.nii.gz affine n2t.txt')
# warping_affine = 'n2t.txt'
# print 'Apply transfo to input landmarks\n...'
# sct.run('isct_antsApplyTransforms 3 ' + cross_native + ' cross_affine.nii.gz -r ' + template_landmark + ' -t ' + warping_affine + ' -n NearestNeighbor')
# print 'Computing transfo between moved landmarks and template landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField cross_template.nii.gz cross_affine.nii.gz warp_affine2temp.nii.gz 5x5x5 3 2 0')
# warping_bspline = 'warp_affine2temp.nii.gz'
# print 'Composing transformations\n...'
# sct.run('isct_ComposeMultiTransform 3 warp_full.nii.gz -r ' + template_landmark + ' ' + warping_bspline + ' ' + warping_affine)
# warping_concat = 'warp_full.nii.gz'
# if final_warp == '' :
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n Linear')
# if final_warp == 'NN':
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n NearestNeighbor')
# if final_warp == 'spline':
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n BSpline[3]')
print '\nFile created : ' + output_name
def main():
# get path of the toolbox
status, path_sct = getstatusoutput('echo $SCT_DIR')
#print path_sct
#Initialization
fname = ''
landmark = ''
verbose = param.verbose
output_name = 'aligned.nii.gz'
template_landmark = ''
final_warp = param.final_warp
compose = param.compose
transfo = 'affine'
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:l:o:R:t:w:c:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-l"):
landmark = arg
elif opt in ("-o"):
output_name = arg
elif opt in ("-R"):
template_landmark = arg
elif opt in ("-t"):
transfo = arg
elif opt in ("-w"):
final_warp = arg
elif opt in ("-c"):
compose = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname == '' or landmark == '' or template_landmark == '' :
usage()
if final_warp not in ['','spline','NN']:
usage()
if transfo not in ['affine','bspline','SyN']:
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
sct.check_file_exist(landmark)
sct.check_file_exist(template_landmark)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Verbose ........................... '+str(verbose)
if transfo == 'affine':
print 'Creating cross using input landmarks\n...'
sct.run('sct_label_utils -i ' + landmark + ' -o ' + 'cross_native.nii.gz -t cross ' )
print 'Creating cross using template landmarks\n...'
sct.run('sct_label_utils -i ' + template_landmark + ' -o ' + 'cross_template.nii.gz -t cross ' )
print 'Computing affine transformation between subject and destination landmarks\n...'
os.system('isct_ANTSUseLandmarkImagesToGetAffineTransform cross_template.nii.gz cross_native.nii.gz affine n2t.txt')
warping = 'n2t.txt'
elif transfo == 'SyN':
warping = 'warp_subject2template.nii.gz'
tmp_name = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+tmp_name)
tmp_abs_path = os.path.abspath(tmp_name)
sct.run('cp ' + landmark + ' ' + tmp_abs_path)
os.chdir(tmp_name)
# sct.run('sct_label_utils -i '+landmark+' -t dist-inter')
# sct.run('sct_label_utils -i '+template_landmark+' -t plan -o template_landmarks_plan.nii.gz -c 5')
# sct.run('sct_crop_image -i template_landmarks_plan.nii.gz -o template_landmarks_plan_cropped.nii.gz -start 0.35,0.35 -end 0.65,0.65 -dim 0,1')
# sct.run('sct_label_utils -i '+landmark+' -t plan -o landmarks_plan.nii.gz -c 5')
# sct.run('sct_crop_image -i landmarks_plan.nii.gz -o landmarks_plan_cropped.nii.gz -start 0.35,0.35 -end 0.65,0.65 -dim 0,1')
# sct.run('isct_antsRegistration --dimensionality 3 --transform SyN[0.5,3,0] --metric MeanSquares[template_landmarks_plan_cropped.nii.gz,landmarks_plan_cropped.nii.gz,1] --convergence 400x200 --shrink-factors 4x2 --smoothing-sigmas 4x2mm --restrict-deformation 0x0x1 --output [landmarks_reg,landmarks_reg.nii.gz] --interpolation NearestNeighbor --float')
# sct.run('isct_c3d -mcs landmarks_reg0Warp.nii.gz -oo warp_vecx.nii.gz warp_vecy.nii.gz warp_vecz.nii.gz')
# sct.run('isct_c3d warp_vecz.nii.gz -resample 200% -o warp_vecz_r.nii.gz')
# sct.run('isct_c3d warp_vecz_r.nii.gz -smooth 0x0x3mm -o warp_vecz_r_sm.nii.gz')
# sct.run('sct_crop_image -i warp_vecz_r_sm.nii.gz -o warp_vecz_r_sm_line.nii.gz -start 0.5,0.5 -end 0.5,0.5 -dim 0,1 -b 0')
# sct.run('sct_label_utils -i warp_vecz_r_sm_line.nii.gz -t plan_ref -o warp_vecz_r_sm_line_extended.nii.gz -c 0 -r '+template_landmark)
# sct.run('isct_c3d '+template_landmark+' warp_vecx.nii.gz -reslice-identity -o warp_vecx_res.nii.gz')
# sct.run('isct_c3d '+template_landmark+' warp_vecy.nii.gz -reslice-identity -o warp_vecy_res.nii.gz')
# sct.run('isct_c3d warp_vecx_res.nii.gz warp_vecy_res.nii.gz warp_vecz_r_sm_line_extended.nii.gz -omc 3 '+warping) # no x?
#new
#put labels of the subject at the center of the image (for plan xOy)
import nibabel
from copy import copy
file_labels_input = nibabel.load(landmark)
hdr_labels_input = file_labels_input.get_header()
data_labels_input = file_labels_input.get_data()
data_labels_middle = copy(data_labels_input)
data_labels_middle *= 0
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(landmark).dim
X,Y,Z = data_labels_input.nonzero()
x_middle = int(round(nx/2.0))
y_middle = int(round(ny/2.0))
for i in range(len(Z)):
data_labels_middle[x_middle, y_middle, Z[i]] = data_labels_input[X[i], Y[i], Z[i]]
img = nibabel.Nifti1Image(data_labels_middle, None, hdr_labels_input)
nibabel.save(img, 'labels_input_middle_xy.nii.gz')
#put labels of the template at the center of the image (for plan xOy) #probably not necessary as already done by average labels
file_labels_template = nibabel.load(template_landmark)
hdr_labels_template = file_labels_template.get_header()
data_labels_template = file_labels_template.get_data()
data_template_middle = copy(data_labels_template)
data_template_middle *= 0
x,y,z = data_labels_template.nonzero()
for i in range(len(Z)):
data_template_middle[x_middle, y_middle, z[i]] = data_labels_template[x[i], y[i], z[i]]
img_template = nibabel.Nifti1Image(data_template_middle, None, hdr_labels_template)
nibabel.save(img_template, 'labels_template_middle_xy.nii.gz')
#estimate Bspline transform to register to template
sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField labels_template_middle_xy.nii.gz labels_input_middle_xy.nii.gz '+ warping+' 40x40x1 5 5 0')
# select centerline of warping field according to z and extend it
sct.run('isct_c3d -mcs '+warping+' -oo warp_vecx.nii.gz warp_vecy.nii.gz warp_vecz.nii.gz')
#sct.run('isct_c3d warp_vecz.nii.gz -resample 200% -o warp_vecz_r.nii.gz')
#sct.run('isct_c3d warp_vecz.nii.gz -smooth 0x0x3mm -o warp_vecz_r_sm.nii.gz')
sct.run('sct_crop_image -i warp_vecz.nii.gz -o warp_vecz_r_sm_line.nii.gz -start 0.5,0.5 -end 0.5,0.5 -dim 0,1 -b 0')
sct.run('sct_label_utils -i warp_vecz_r_sm_line.nii.gz -t plan_ref -o warp_vecz_r_sm_line_extended.nii.gz -r '+template_landmark)
sct.run('isct_c3d '+template_landmark+' warp_vecx.nii.gz -reslice-identity -o warp_vecx_res.nii.gz')
sct.run('isct_c3d '+template_landmark+' warp_vecy.nii.gz -reslice-identity -o warp_vecy_res.nii.gz')
sct.run('isct_c3d warp_vecx_res.nii.gz warp_vecy_res.nii.gz warp_vecz_r_sm_line_extended.nii.gz -omc 3 '+warping)
# check results
#dilate first labels
sct.run('fslmaths labels_input_middle_xy.nii.gz -dilF landmark_dilated.nii.gz') #new
sct.run('sct_apply_transfo -i landmark_dilated.nii.gz -o label_moved.nii.gz -d labels_template_middle_xy.nii.gz -w '+warping+' -x nn')
#undilate
sct.run('sct_label_utils -i label_moved.nii.gz -t cubic-to-point -o label_moved_2point.nii.gz')
sct.run('sct_label_utils -i labels_template_middle_xy.nii.gz -r label_moved_2point.nii.gz -o template_removed.nii.gz -t remove')
#end new
# check results
#dilate first labels
#sct.run('fslmaths '+landmark+' -dilF landmark_dilated.nii.gz') #old
#sct.run('sct_apply_transfo -i landmark_dilated.nii.gz -o label_moved.nii.gz -d '+template_landmark+' -w '+warping+' -x nn') #old
#undilate
#sct.run('sct_label_utils -i label_moved.nii.gz -t cubic-to-point -o label_moved_2point.nii.gz') #old
#sct.run('sct_label_utils -i '+template_landmark+' -r label_moved_2point.nii.gz -o template_removed.nii.gz -t remove') #old
# # sct.run('sct_apply_transfo -i '+landmark+' -o label_moved.nii.gz -d '+template_landmark+' -w '+warping+' -x nn')
# # sct.run('sct_label_utils -i '+template_landmark+' -r label_moved.nii.gz -o template_removed.nii.gz -t remove')
# # status, output = sct.run('sct_label_utils -i label_moved.nii.gz -r template_removed.nii.gz -t MSE')
status, output = sct.run('sct_label_utils -i label_moved_2point.nii.gz -r template_removed.nii.gz -t MSE')
sct.printv(output,1,'info')
remove_temp_files = False
if os.path.isfile('error_log_label_moved.txt'):
remove_temp_files = False
with open('log.txt', 'a') as log_file:
log_file.write('Error for '+fname+'\n')
# Copy warping into parent folder
sct.run('cp '+ warping+' ../'+warping)
os.chdir('..')
if remove_temp_files:
sct.run('rm -rf '+tmp_name)
# if transfo == 'bspline' :
# print 'Computing bspline transformation between subject and destination landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField cross_template.nii.gz cross_native.nii.gz warp_ntotemp.nii.gz 5x5x5 3 2 0')
# warping = 'warp_ntotemp.nii.gz'
# if final_warp == '' :
# print 'Apply transfo to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping + ' -n Linear')
# if final_warp == 'NN':
# print 'Apply transfo to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping + ' -n NearestNeighbor')
if final_warp == 'spline':
print 'Apply transfo to input image\n...'
sct.run('sct_apply_transfo -i ' + fname + ' -o ' + output_name + ' -d ' + template_landmark + ' -w ' + warping + ' -x spline')
# Remove warping
os.remove(warping)
# if compose :
# print 'Computing affine transformation between subject and destination landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetAffineTransform cross_template.nii.gz cross_native.nii.gz affine n2t.txt')
# warping_affine = 'n2t.txt'
# print 'Apply transfo to input landmarks\n...'
# sct.run('isct_antsApplyTransforms 3 ' + cross_native + ' cross_affine.nii.gz -r ' + template_landmark + ' -t ' + warping_affine + ' -n NearestNeighbor')
# print 'Computing transfo between moved landmarks and template landmarks\n...'
# sct.run('isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField cross_template.nii.gz cross_affine.nii.gz warp_affine2temp.nii.gz 5x5x5 3 2 0')
# warping_bspline = 'warp_affine2temp.nii.gz'
# print 'Composing transformations\n...'
# sct.run('isct_ComposeMultiTransform 3 warp_full.nii.gz -r ' + template_landmark + ' ' + warping_bspline + ' ' + warping_affine)
# warping_concat = 'warp_full.nii.gz'
# if final_warp == '' :
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n Linear')
# if final_warp == 'NN':
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n NearestNeighbor')
# if final_warp == 'spline':
# print 'Apply concat warp to input image\n...'
# sct.run('isct_antsApplyTransforms 3 ' + fname + ' ' + output_name + ' -r ' + template_landmark + ' -t ' + warping_concat + ' -n BSpline[3]')
print '\nFile created : ' + output_name
def main():
# Initialization
fname_data = ''
interp_factor = param.interp_factor
remove_temp_files = param.remove_temp_files
verbose = param.verbose
suffix = param.suffix
smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# get path of the toolbox
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
# Parameters for debug mode
if param.debug:
fname_data = os.path.join(path_sct, 'testing', 'data', 'errsm_23',
't2', 't2_manual_segmentation.nii.gz')
remove_temp_files = 0
param.mask_size = 10
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:v:r:s:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-s'):
smoothing_sigma = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' segmentation ........... ' + fname_data)
sct.printv(' interp factor .......... ' + str(interp_factor))
sct.printv(' smoothing sigma ........ ' + str(smoothing_sigma))
# check existence of input files
sct.printv('\nCheck existence of input files...')
sct.check_file_exist(fname_data, verbose)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_tmp = sct.tmp_create(basename="binary_to_trilinear", verbose=verbose)
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...',
param.verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# upsample data
sct.printv('\nUpsample data...', verbose)
sct.run([
"sct_resample", "-i", "data.nii", "-x", "linear", "-vox",
str(nx * interp_factor) + 'x' + str(ny * interp_factor) + 'x' +
str(nz * interp_factor), "-o", "data_up.nii"
], verbose)
# Smooth along centerline
sct.printv('\nSmooth along centerline...', verbose)
sct.run([
"sct_smooth_spinalcord", "-i", "data_up.nii", "-s", "data_up.nii",
"-smooth",
str(smoothing_sigma), "-r",
str(remove_temp_files), "-v",
str(verbose)
], verbose)
# downsample data
sct.printv('\nDownsample data...', verbose)
sct.run([
"sct_resample", "-i", "data_up_smooth.nii", "-x", "linear", "-vox",
str(nx) + 'x' + str(ny) + 'x' + str(nz), "-o",
"data_up_smooth_down.nii"
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...')
fname_out = sct.generate_output_file(
os.path.join(path_tmp, "data_up_smooth_down.nii"),
'' + file_data + suffix + ext_data)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) +
's')
# to view results
sct.printv('\nTo view results, type:')
sct.printv('fslview ' + file_data + ' ' + file_data + suffix + ' &\n')
def main():
# get default parameters
step1 = Paramreg(step='1', type='seg', algo='slicereg', metric='MeanSquares', iter='10')
step2 = Paramreg(step='2', type='im', algo='syn', metric='MI', iter='3')
# step1 = Paramreg()
paramreg = ParamregMultiStep([step1, step2])
# step1 = Paramreg_step(step='1', type='seg', algo='bsplinesyn', metric='MeanSquares', iter='10', shrink='1', smooth='0', gradStep='0.5')
# step2 = Paramreg_step(step='2', type='im', algo='syn', metric='MI', iter='10', shrink='1', smooth='0', gradStep='0.5')
# paramreg = ParamregMultiStep([step1, step2])
# Initialize the parser
parser = Parser(__file__)
parser.usage.set_description('Register anatomical image to the template.')
parser.add_option(name="-i",
type_value="file",
description="Anatomical image.",
mandatory=True,
example="anat.nii.gz")
parser.add_option(name="-s",
type_value="file",
description="Spinal cord segmentation.",
mandatory=True,
example="anat_seg.nii.gz")
parser.add_option(name="-l",
type_value="file",
description="Labels. See: http://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/",
mandatory=True,
default_value='',
example="anat_labels.nii.gz")
parser.add_option(name="-t",
type_value="folder",
description="Path to MNI-Poly-AMU template.",
mandatory=False,
default_value=param.path_template)
parser.add_option(name="-p",
type_value=[[':'], 'str'],
description="""Parameters for registration (see sct_register_multimodal). Default:\n--\nstep=1\ntype="""+paramreg.steps['1'].type+"""\nalgo="""+paramreg.steps['1'].algo+"""\nmetric="""+paramreg.steps['1'].metric+"""\npoly="""+paramreg.steps['1'].poly+"""\n--\nstep=2\ntype="""+paramreg.steps['2'].type+"""\nalgo="""+paramreg.steps['2'].algo+"""\nmetric="""+paramreg.steps['2'].metric+"""\niter="""+paramreg.steps['2'].iter+"""\nshrink="""+paramreg.steps['2'].shrink+"""\nsmooth="""+paramreg.steps['2'].smooth+"""\ngradStep="""+paramreg.steps['2'].gradStep+"""\n--""",
mandatory=False,
example="step=2,type=seg,algo=bsplinesyn,metric=MeanSquares,iter=5,shrink=2:step=3,type=im,algo=syn,metric=MI,iter=5,shrink=1,gradStep=0.3")
parser.add_option(name="-r",
type_value="multiple_choice",
description="""Remove temporary files.""",
mandatory=False,
default_value='1',
example=['0', '1'])
parser.add_option(name="-v",
type_value="multiple_choice",
description="""Verbose. 0: nothing. 1: basic. 2: extended.""",
mandatory=False,
default_value=param.verbose,
example=['0', '1', '2'])
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = '/Users/julien/data/temp/sct_example_data/t2/t2.nii.gz'
fname_landmarks = '/Users/julien/data/temp/sct_example_data/t2/labels.nii.gz'
fname_seg = '/Users/julien/data/temp/sct_example_data/t2/t2_seg.nii.gz'
path_template = param.path_template
remove_temp_files = 0
verbose = 2
# speed = 'superfast'
#param_reg = '2,BSplineSyN,0.6,MeanSquares'
else:
arguments = parser.parse(sys.argv[1:])
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
path_template = arguments['-t']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
if '-p' in arguments:
paramreg_user = arguments['-p']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
# initialize other parameters
file_template = param.file_template
file_template_label = param.file_template_label
file_template_seg = param.file_template_seg
output_type = param.output_type
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# get absolute path - TO DO: remove! NEVER USE ABSOLUTE PATH...
path_template = os.path.abspath(path_template)
# get fname of the template + template objects
fname_template = sct.slash_at_the_end(path_template, 1)+file_template
fname_template_label = sct.slash_at_the_end(path_template, 1)+file_template_label
fname_template_seg = sct.slash_at_the_end(path_template, 1)+file_template_seg
# check file existence
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_label, verbose)
sct.check_file_exist(fname_template_seg, verbose)
# print arguments
sct.printv('\nCheck parameters:', verbose)
sct.printv('.. Data: '+fname_data, verbose)
sct.printv('.. Landmarks: '+fname_landmarks, verbose)
sct.printv('.. Segmentation: '+fname_seg, verbose)
sct.printv('.. Path template: '+path_template, verbose)
sct.printv('.. Output type: '+str(output_type), verbose)
sct.printv('.. Remove temp files: '+str(remove_temp_files), verbose)
sct.printv('\nParameters for registration:')
for pStep in range(1, len(paramreg.steps)+1):
sct.printv('Step #'+paramreg.steps[str(pStep)].step, verbose)
sct.printv('.. Type #'+paramreg.steps[str(pStep)].type, verbose)
sct.printv('.. Algorithm................ '+paramreg.steps[str(pStep)].algo, verbose)
sct.printv('.. Metric................... '+paramreg.steps[str(pStep)].metric, verbose)
sct.printv('.. Number of iterations..... '+paramreg.steps[str(pStep)].iter, verbose)
sct.printv('.. Shrink factor............ '+paramreg.steps[str(pStep)].shrink, verbose)
sct.printv('.. Smoothing factor......... '+paramreg.steps[str(pStep)].smooth, verbose)
sct.printv('.. Gradient step............ '+paramreg.steps[str(pStep)].gradStep, verbose)
sct.printv('.. Degree of polynomial..... '+paramreg.steps[str(pStep)].poly, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
sct.printv('\nCheck input labels...')
# check if label image contains coherent labels
image_label = Image(fname_landmarks)
# -> all labels must be different
labels = image_label.getNonZeroCoordinates(sorting='value')
hasDifferentLabels = True
for lab in labels:
for otherlabel in labels:
if lab != otherlabel and lab.hasEqualValue(otherlabel):
hasDifferentLabels = False
break
if not hasDifferentLabels:
sct.printv('ERROR: Wrong landmarks input. All labels must be different.', verbose, 'error')
# all labels must be available in tempalte
image_label_template = Image(fname_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondance in tempalte space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
status, output = sct.run('mkdir '+path_tmp)
# copy files to temporary folder
sct.printv('\nCopy files...', verbose)
sct.run('isct_c3d '+fname_data+' -o '+path_tmp+'/data.nii')
sct.run('isct_c3d '+fname_landmarks+' -o '+path_tmp+'/landmarks.nii.gz')
sct.run('isct_c3d '+fname_seg+' -o '+path_tmp+'/segmentation.nii.gz')
sct.run('isct_c3d '+fname_template+' -o '+path_tmp+'/template.nii')
sct.run('isct_c3d '+fname_template_label+' -o '+path_tmp+'/template_labels.nii.gz')
sct.run('isct_c3d '+fname_template_seg+' -o '+path_tmp+'/template_seg.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('isct_c3d data.nii -resample-mm 1.0x1.0x1.0mm -interpolation Linear -o datar.nii')
sct.run('isct_c3d segmentation.nii.gz -resample-mm 1.0x1.0x1.0mm -interpolation NearestNeighbor -o segmentationr.nii.gz')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels('landmarks.nii.gz', 'datar.nii', 'landmarksr.nii.gz')
# # TODO
# sct.run('sct_label_utils -i datar.nii -t create -x 124,186,19,2:129,98,23,8 -o landmarksr.nii.gz')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
set_orientation('datar.nii', 'RPI', 'data_rpi.nii')
set_orientation('landmarksr.nii.gz', 'RPI', 'landmarks_rpi.nii.gz')
set_orientation('segmentationr.nii.gz', 'RPI', 'segmentation_rpi.nii.gz')
# # Change orientation of input images to RPI
# sct.printv('\nChange orientation of input images to RPI...', verbose)
# set_orientation('data.nii', 'RPI', 'data_rpi.nii')
# set_orientation('landmarks.nii.gz', 'RPI', 'landmarks_rpi.nii.gz')
# set_orientation('segmentation.nii.gz', 'RPI', 'segmentation_rpi.nii.gz')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
sct.run('sct_crop_image -i segmentation_rpi.nii.gz -o segmentation_rpi_crop.nii.gz -dim 2 -bzmax')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
sct.run('sct_straighten_spinalcord -i segmentation_rpi_crop.nii.gz -c segmentation_rpi_crop.nii.gz -r 0 -v '+str(verbose), verbose)
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d data_rpi.nii -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -t remove -i template_labels.nii.gz -o template_label.nii.gz -r landmarks_rpi.nii.gz')
# Make sure landmarks are INT
sct.printv('\nConvert landmarks to INT...', verbose)
sct.run('isct_c3d template_label.nii.gz -type int -o template_label.nii.gz', verbose)
# Create a cross for the template labels - 5 mm
sct.printv('\nCreate a 5 mm cross for the template labels...', verbose)
sct.run('sct_label_utils -t cross -i template_label.nii.gz -o template_label_cross.nii.gz -c 5')
# Create a cross for the input labels and dilate for straightening preparation - 5 mm
sct.printv('\nCreate a 5mm cross for the input labels and dilate for straightening preparation...', verbose)
sct.run('sct_label_utils -t cross -i landmarks_rpi.nii.gz -o landmarks_rpi_cross3x3.nii.gz -c 5 -d')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i landmarks_rpi_cross3x3.nii.gz -o landmarks_rpi_cross3x3_straight.nii.gz -d segmentation_rpi_crop_straight.nii.gz -w warp_curve2straight.nii.gz -x nn')
# Convert landmarks from FLOAT32 to INT
sct.printv('\nConvert landmarks from FLOAT32 to INT...', verbose)
sct.run('isct_c3d landmarks_rpi_cross3x3_straight.nii.gz -type int -o landmarks_rpi_cross3x3_straight.nii.gz')
# Remove labels that do not correspond with each others.
sct.printv('\nRemove labels that do not correspond with each others.', verbose)
sct.run('sct_label_utils -t remove-symm -i landmarks_rpi_cross3x3_straight.nii.gz -o landmarks_rpi_cross3x3_straight.nii.gz,template_label_cross.nii.gz -r template_label_cross.nii.gz')
# Estimate affine transfo: straight --> template (landmark-based)'
sct.printv('\nEstimate affine transfo: straight anat --> template (landmark-based)...', verbose)
# converting landmarks straight and curved to physical coordinates
image_straight = Image('landmarks_rpi_cross3x3_straight.nii.gz')
landmark_straight = image_straight.getNonZeroCoordinates(sorting='value')
image_template = Image('template_label_cross.nii.gz')
landmark_template = image_template.getNonZeroCoordinates(sorting='value')
# Reorganize landmarks
points_fixed, points_moving = [], []
landmark_straight_mean = []
for coord in landmark_straight:
if coord.value not in [c.value for c in landmark_straight_mean]:
temp_landmark = coord
temp_number = 1
for other_coord in landmark_straight:
if coord.hasEqualValue(other_coord) and coord != other_coord:
temp_landmark += other_coord
temp_number += 1
landmark_straight_mean.append(temp_landmark / temp_number)
for coord in landmark_straight_mean:
point_straight = image_straight.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_moving.append([point_straight[0][0], point_straight[0][1], point_straight[0][2]])
for coord in landmark_template:
point_template = image_template.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_fixed.append([point_template[0][0], point_template[0][1], point_template[0][2]])
# Register curved landmarks on straight landmarks based on python implementation
sct.printv('\nComputing rigid transformation (algo=translation-scaling-z) ...', verbose)
import msct_register_landmarks
(rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = \
msct_register_landmarks.getRigidTransformFromLandmarks(
points_fixed, points_moving, constraints='translation-scaling-z', show=False)
# writing rigid transformation file
text_file = open("straight2templateAffine.txt", "w")
text_file.write("#Insight Transform File V1.0\n")
text_file.write("#Transform 0\n")
text_file.write("Transform: FixedCenterOfRotationAffineTransform_double_3_3\n")
text_file.write("Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\n" % (
1.0/rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],
rotation_matrix[1, 0], 1.0/rotation_matrix[1, 1], rotation_matrix[1, 2],
rotation_matrix[2, 0], rotation_matrix[2, 1], 1.0/rotation_matrix[2, 2],
translation_array[0, 0], translation_array[0, 1], -translation_array[0, 2]))
text_file.write("FixedParameters: %.9f %.9f %.9f\n" % (points_moving_barycenter[0],
points_moving_barycenter[1],
points_moving_barycenter[2]))
text_file.close()
# Apply affine transformation: straight --> template
sct.printv('\nApply affine transformation: straight --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
sct.run('sct_apply_transfo -i data_rpi.nii -o data_rpi_straight2templateAffine.nii -d template.nii -w warp_curve2straightAffine.nii.gz')
sct.run('sct_apply_transfo -i segmentation_rpi.nii.gz -o segmentation_rpi_straight2templateAffine.nii.gz -d template.nii -w warp_curve2straightAffine.nii.gz -x linear')
# threshold to 0.5
nii = Image('segmentation_rpi_straight2templateAffine.nii.gz')
data = nii.data
data[data < 0.5] = 0
nii.data = data
nii.setFileName('segmentation_rpi_straight2templateAffine_th.nii.gz')
nii.save()
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax('segmentation_rpi_straight2templateAffine_th.nii.gz')
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i template.nii -o template_crop.nii -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
sct.run('sct_crop_image -i template_seg.nii.gz -o template_seg_crop.nii.gz -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
sct.run('sct_crop_image -i data_rpi_straight2templateAffine.nii -o data_rpi_straight2templateAffine_crop.nii -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
sct.run('sct_crop_image -i segmentation_rpi_straight2templateAffine.nii.gz -o segmentation_rpi_straight2templateAffine_crop.nii.gz -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i template_crop.nii -o template_crop_r.nii -f 1x1x'+zsubsample, verbose)
sct.run('sct_resample -i template_seg_crop.nii.gz -o template_seg_crop_r.nii.gz -f 1x1x'+zsubsample, verbose)
sct.run('sct_resample -i data_rpi_straight2templateAffine_crop.nii -o data_rpi_straight2templateAffine_crop_r.nii -f 1x1x'+zsubsample, verbose)
sct.run('sct_resample -i segmentation_rpi_straight2templateAffine_crop.nii.gz -o segmentation_rpi_straight2templateAffine_crop_r.nii.gz -f 1x1x'+zsubsample, verbose)
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)+1):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = 'data_rpi_straight2templateAffine_crop_r.nii'
dest = 'template_crop_r.nii'
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'segmentation_rpi_straight2templateAffine_crop_r.nii.gz'
dest = 'template_seg_crop_r.nii.gz'
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
src = sct.add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# Apply warping fields to anat and template
if output_type == 1:
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -c 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -c 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'/warp_template2anat.nii.gz', 'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'/warp_anat2template.nii.gz', 'warp_anat2template.nii.gz', verbose)
if output_type == 1:
sct.generate_output_file(path_tmp+'/template2anat.nii.gz', 'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'/anat2template.nii.gz', 'anat2template'+ext_data, verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_data+' template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 anat2template &\n', verbose, 'info')
def main():
# Initialization
fname_src = ''
fname_transfo = ''
path_out = 'atlas'
verbose = param.verbose
start_time = time.time()
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
print path_sct
# Parameters for debug mode
if param.debug:
fname_src = os.path.expanduser("~")+'/code/spinalcordtoolbox_dev/testing/data/errsm_23/mt/mtr.nii.gz'
fname_transfo = os.path.expanduser("~")+'/code/spinalcordtoolbox_dev/testing/data/errsm_23/template/warp_template2mt.nii.gz'
verbose = 1
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'hd:w:o:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-d"):
fname_src = arg
elif opt in ("-o"):
path_out = arg
elif opt in ("-w"):
fname_transfo = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_src == '' or fname_transfo == '':
usage()
# check existence of input files
sct.check_file_exist(fname_src)
sct.check_file_exist(fname_transfo)
# print arguments
print '\nCheck parameters:'
print '.. Metric image: '+fname_src
print '.. Transformation: '+fname_transfo
print '.. Output folder: '+path_out
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
# create output folder
if os.path.exists(path_out):
sct.run('rm -rf '+path_out)
sct.run('mkdir '+path_out)
# get atlas files
status, output = sct.run('ls '+path_sct+'/data/atlas/vol*.nii.gz')
file_atlas_list = output.split()
# Warp atlas
for i in xrange (0,len(file_atlas_list)):
path_atlas, file_atlas, ext_atlas = sct.extract_fname(file_atlas_list[i])
sct.run('WarpImageMultiTransform 3 '+file_atlas_list[i]+' '+path_out+'/'+file_atlas+ext_atlas+' -R '+fname_src+' '+fname_transfo)
# Copy list.txt
sct.run('cp '+path_sct+'/data/atlas/list.txt '+path_out+'/')
# Warp other template objects
sct.run('WarpImageMultiTransform 3 '+path_sct+'/data/template/MNI-Poly-AMU_GM.nii.gz '+path_out+'/../gray_matter.nii.gz -R '+fname_src+' '+fname_transfo)
sct.run('WarpImageMultiTransform 3 '+path_sct+'/data/template/MNI-Poly-AMU_WM.nii.gz '+path_out+'/../white_matter.nii.gz -R '+fname_src+' '+fname_transfo)
sct.run('WarpImageMultiTransform 3 '+path_sct+'/data/template/MNI-Poly-AMU_level.nii.gz '+path_out+'/../vertebral_labeling.nii.gz -R '+fname_src+' --use-NN '+fname_transfo)
sct.run('WarpImageMultiTransform 3 '+path_sct+'/data/template/MNI-Poly-AMU_CSF.nii.gz '+path_out+'/../csf.nii.gz -R '+fname_src+' --use-NN '+fname_transfo)
def create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data +
ext_data)
path_tmp = sct.tmp_create(basename="create_mask", verbose=param.verbose)
sct.printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
sct.printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(
os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv('\nDimensions:', param.verbose)
sct.printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv(
'WARNING in ' + os.path.basename(__file__) +
': Input image is 4d but output mask will be 3D from first time slice.',
param.verbose, 'warning')
# extract first volume to have 3d reference
nii = msct_image.empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# get file name
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(
['sct_label_utils', '-i', 'point_RPI.nii', '-display'],
verbose=param.verbose)
# parse to get coordinate
# TODO fixup... this is quite magic
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
np.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param,
center,
param.shape,
param.size,
im_data=im_data)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(path_tmp)
sct.display_viewer_syntax([param.fname_data, param.fname_out],
colormaps=['gray', 'red'],
opacities=['', '0.5'])
def main():
#Initialization
fname = ''
fname_centerline = ''
mean_intensity = param.mean_intensity
verbose = param.verbose
padding = param.padding
window_length = param.window_length
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:c:v:p:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-c"):
fname_centerline = arg
elif opt in ("-p"):
window_length = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
#if fname == '' or fname_centerline == '':
if fname == '':
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
#sct.check_file_exist(fname_centerline)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Centerline ...................... '+fname_centerline
print' Verbose ........................... '+str(verbose)
# Extract path, file and extension
path_input, file_input, ext_input = sct.extract_fname(fname)
sct.printv('\nOpen volume...',verbose)
file = nibabel.load(fname)
data = file.get_data()
hdr = file.get_header()
if fname_centerline != '':
## [Process 1] Command for extracting center of mass for each slice of the centerline file if provided
sct.printv('\nOpen centerline...',verbose)
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = Image(fname_centerline).dim
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
file_c = nibabel.load(fname_centerline)
data_c = file_c.get_data()
#X,Y,Z = (data_c>0).nonzero()
#min_z_index, max_z_index = min(Z), max(Z)
z_centerline = [iz for iz in range(0, nz, 1) if data_c[:,:,iz].any() ]
nz_nonz = len(z_centerline)
if nz_nonz==0 :
print '\nERROR: Centerline is empty'
sys.exit()
x_centerline = [0 for iz in range(0, nz_nonz, 1)]
y_centerline = [0 for iz in range(0, nz_nonz, 1)]
#print("z_centerline", z_centerline,nz_nonz,len(x_centerline))
print '\nGet center of mass of the centerline ...'
for iz in xrange(len(z_centerline)):
x_centerline[iz], y_centerline[iz] = ndimage.measurements.center_of_mass(np.array(data_c[:,:,z_centerline[iz]]))
# end of Process 1
## [Process 2] Process for defining the middle vertical line as reference for normalizing the intensity of the image
if fname_centerline == '':
print '\nGet dimensions of input image...'
nx, ny, nz, nt, px, py, pz, pt = Image(fname).dim
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
z_centerline = [iz for iz in range(0, nz, 1)]
nz_nonz = len(z_centerline)
x_middle = int(round(nx/2))
y_middle = int(round(ny/2))
x_centerline = [x_middle for iz in range(0, nz, 1)]
y_centerline = [y_middle for iz in range(0, nz, 1)]
# end of Process 2
means = [0 for iz in range(0, nz_nonz, 1)]
print '\nGet mean intensity along the centerline ...'
for iz in xrange(len(z_centerline)):
means[iz] = np.mean(data[(int(round(x_centerline[iz]))-padding):(int(round(x_centerline[iz]))+padding),(int(round(y_centerline[iz]))-padding):(int(round(y_centerline[iz]))+padding),z_centerline[iz]])
# print('\nSmoothing results with spline...')
# # Smoothing with scipy library (Julien Touati's code)
# m =np.mean(means)
# sigma = np.std(means)
# smoothing_param = (((m + np.sqrt(2*m))*(sigma**2))+((m - np.sqrt(2*m))*(sigma**2)))/2
# #Equivalent to : m*sigma**2
# tck = splrep(z_centerline, means, s=smoothing_param)
# means_smooth = splev(z_centerline, tck)
# Smoothing with low-pass filter
print '\nSmoothing with lowpass filter: butterworth order 5...'
from msct_smooth import lowpass
means_smooth = lowpass(means)
# #Smoothing with nurbs
#points = [[means[n],0, z_centerline[n]] for n in range(len(z_centerline))]
#nurbs = NURBS(3,1000,points)
#P = nurbs.getCourbe3D()
#means_smooth=P[0] #size of means_smooth? should be bigger than len(z_centerline)
# #Smoothing with hanning
# print('\nSmoothing results with hanning windowing...')
# means = np.asarray(means)
# means_smooth = smoothing_window(means, window_len=window_length)
# print means.shape[0], means_smooth.shape[0]
if verbose :
plt.figure()
#plt.subplot(2,1,1)
plt.plot(z_centerline,means, "ro")
#plt.subplot(2,1,2)
plt.plot(means_smooth)
plt.title("Mean intensity: Type of window: hanning Window_length= %d mm" % window_length)
plt.show()
print('\nNormalizing intensity along centerline...')
#Define extended meaned intensity for all the spinal cord
means_smooth_extended = [0 for i in range(0, data.shape[2], 1)]
for iz in range(len(z_centerline)):
means_smooth_extended[z_centerline[iz]] = means_smooth[iz]
X_means_smooth_extended = np.nonzero(means_smooth_extended)
X_means_smooth_extended = np.transpose(X_means_smooth_extended)
if len(X_means_smooth_extended) != 0:
means_smooth_extended[0] = means_smooth_extended[X_means_smooth_extended[0]]
means_smooth_extended[-1] = means_smooth_extended[X_means_smooth_extended[-1]]
#Add two rows to the vector X_mask_completed:
# one before as mask_completed[0] is now diff from 0
# one after as mask_completed[-1] is now diff from 0
X_means_smooth_extended = np.append(X_means_smooth_extended, len(means_smooth_extended)-1)
X_means_smooth_extended = np.insert(X_means_smooth_extended, 0, 0)
#linear interpolation
count_zeros=0
for i in range(1,len(means_smooth_extended)-1):
if means_smooth_extended[i]==0:
means_smooth_extended[i] = 0.5 * (means_smooth_extended[X_means_smooth_extended[i-1-count_zeros]] + means_smooth_extended[X_means_smooth_extended[i-count_zeros]]) # linear interpolation with closest non zero points
#redefine X_mask_completed
X_means_smooth_extended = np.nonzero(means_smooth_extended)
X_means_smooth_extended = np.transpose(X_means_smooth_extended)
#recurrence
# count_zeros=0
# for i in range(1,len(means_smooth_extended)-1):
# if means_smooth_extended[i]==0:
# means_smooth_extended[i] = 0.5*(means_smooth_extended[X_means_smooth_extended[i-1-count_zeros]] + means_smooth_extended[X_means_smooth_extended[i-count_zeros]])
# # redefine X_mask_extended
# X_mask_completed = np.nonzero(means_smooth_extended)
# X_mask_completed = np.transpose(X_mask_completed)
# #count_zeros += 1
if verbose :
plt.figure()
plt.subplot(2,1,1)
plt.plot(z_centerline,means)
plt.plot(z_centerline,means_smooth)
plt.title("Mean intensity")
plt.subplot(2,1,2)
plt.plot(z_centerline,means)
plt.plot(means_smooth_extended)
plt.title("Extended mean intensity")
plt.show()
for i in range(data.shape[2]):
data[:,:,i] = data[:,:,i] * (mean_intensity/means_smooth_extended[i])
hdr.set_data_dtype('uint16') # set imagetype to uint16
# save volume
sct.printv('\nWrite NIFTI volumes...',verbose)
data = data.astype(np.float32, copy =False)
img = nibabel.Nifti1Image(data, None, hdr)
output_name = file_input+'_normalized'+ext_input
nibabel.save(img,output_name)
sct.printv('\n.. File created:' + output_name,verbose)
print('\nNormalizing overall intensity...')
# sct.run('fslmaths ' + output_name + ' -inm ' + str(mean_intensity) + ' ' + output_name)
# to view results
print '\nDone !'
print '\nTo view results, type:'
print 'fslview '+output_name+' &\n'
def main():
parser = get_parser()
param = Param()
args = sys.argv[1:]
arguments = parser.parse(args)
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
path_template = sct.slash_at_the_end(arguments['-t'], 1)
contrast_template = arguments['-c']
ref = arguments['-ref']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
# file_template_label = param.file_template_label
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# retrieve template file names
from sct_warp_template import get_file_label
file_template_vertebral_labeling = get_file_label(path_template + 'template/', 'vertebral')
file_template = get_file_label(path_template + 'template/', contrast_template.upper() + '-weighted')
file_template_seg = get_file_label(path_template + 'template/', 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = path_template + 'template/' + file_template
fname_template_vertebral_labeling = path_template + 'template/' + file_template_vertebral_labeling
fname_template_seg = path_template + 'template/' + file_template_seg
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# print arguments
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(remove_temp_files), verbose)
# create QC folder
sct.create_folder(param.path_qc)
# check if data, segmentation and landmarks are in the same space
# JULIEN 2017-04-25: removed because of issue #1168
# sct.printv('\nCheck if data, segmentation and landmarks are in the same space...')
# if not sct.check_if_same_space(fname_data, fname_seg):
# sct.printv('ERROR: Data image and segmentation are not in the same space. Please check space and orientation of your files', verbose, 'error')
# if not sct.check_if_same_space(fname_data, fname_landmarks):
# sct.printv('ERROR: Data image and landmarks are not in the same space. Please check space and orientation of your files', verbose, 'error')
# check input labels
labels = check_labels(fname_landmarks)
# create temporary folder
path_tmp = sct.tmp_create(verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i ' + fname_data + ' -o ' + path_tmp + ftmp_data)
sct.run('sct_convert -i ' + fname_seg + ' -o ' + path_tmp + ftmp_seg)
sct.run('sct_convert -i ' + fname_landmarks + ' -o ' + path_tmp + ftmp_label)
sct.run('sct_convert -i ' + fname_template + ' -o ' + path_tmp + ftmp_template)
sct.run('sct_convert -i ' + fname_template_seg + ' -o ' + path_tmp + ftmp_template_seg)
# sct.run('sct_convert -i '+fname_template_label+' -o '+path_tmp+ftmp_template_label)
# go to tmp folder
os.chdir(path_tmp)
# copy header of anat to segmentation (issue #1168)
# from sct_image import copy_header
# im_data = Image(ftmp_data)
# im_seg = Image(ftmp_seg)
# copy_header(im_data, im_seg)
# im_seg.save()
# im_label = Image(ftmp_label)
# copy_header(im_data, im_label)
# im_label.save()
# Generate labels from template vertebral labeling
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
sct.run('sct_label_utils -i ' + fname_template_vertebral_labeling + ' -vert-body 0 -o ' + ftmp_template_label)
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
sct.run('sct_maths -i seg.nii.gz -bin 0.5 -o seg.nii.gz')
# smooth segmentation (jcohenadad, issue #613)
# sct.printv('\nSmooth segmentation...', verbose)
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
# jcohenadad: updated 2016-06-16: DO NOT smooth the seg anymore. Issue #
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 0 -o '+add_suffix(ftmp_seg, '_smooth'))
# ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i ' + ftmp_data + ' -mm 1.0x1.0x1.0 -x linear -o ' + add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i ' + ftmp_seg + ' -mm 1.0x1.0x1.0 -x linear -o ' + add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i ' + ftmp_data + ' -setorient RPI -o ' + add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i ' + ftmp_seg + ' -setorient RPI -o ' + add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i ' + ftmp_label + ' -setorient RPI -o ' + add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_crop') + ' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
if os.path.isfile('../warp_curve2straight.nii.gz') and os.path.isfile('../warp_straight2curve.nii.gz') and os.path.isfile('../straight_ref.nii.gz'):
# if they exist, copy them into current folder
sct.printv('WARNING: Straightening was already run previously. Copying warping fields...', verbose, 'warning')
shutil.copy('../warp_curve2straight.nii.gz', 'warp_curve2straight.nii.gz')
shutil.copy('../warp_straight2curve.nii.gz', 'warp_straight2curve.nii.gz')
shutil.copy('../straight_ref.nii.gz', 'straight_ref.nii.gz')
# apply straightening
sct.run('sct_apply_transfo -i ' + ftmp_seg + ' -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o ' + add_suffix(ftmp_seg, '_straight'))
else:
sct.run('sct_straighten_spinalcord -i ' + ftmp_seg + ' -s ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_straight') + ' -qc 0 -r 0 -v ' + str(verbose), verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d ' + ftmp_data + ' -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i ' + ftmp_template_label + ' -o ' + ftmp_template_label + ' -remove ' + ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i ' + ftmp_label + ' -o ' + add_suffix(ftmp_label, '_dilate') + ' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i ' + ftmp_label + ' -o ' + add_suffix(ftmp_label, '_straight') + ' -d ' + add_suffix(ftmp_seg, '_straight') + ' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof, fname_affine='straight2templateAffine.txt', verbose=verbose)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i ' + ftmp_data + ' -o ' + add_suffix(ftmp_data, '_straightAffine') + ' -d ' + ftmp_template + ' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_straightAffine') + ' -d ' + ftmp_template + ' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(round(np.min(points_straight))), int(round(np.max(points_straight)))
sct.run('sct_crop_image -i ' + ftmp_seg + ' -start ' + str(min_point) + ' -end ' + str(max_point) + ' -dim 2 -b 0 -o ' + add_suffix(ftmp_seg, '_black'))
ftmp_seg = add_suffix(ftmp_seg, '_black')
"""
# binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i ' + ftmp_seg + ' -bin 0.5 -o ' + add_suffix(ftmp_seg, '_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i ' + ftmp_template + ' -o ' + add_suffix(ftmp_template, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i ' + ftmp_template_seg + ' -o ' + add_suffix(ftmp_template_seg, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i ' + ftmp_data + ' -o ' + add_suffix(ftmp_data, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i ' + ftmp_template + ' -o ' + add_suffix(ftmp_template, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i ' + ftmp_template_seg + ' -o ' + add_suffix(ftmp_template_seg, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i ' + ftmp_data + ' -o ' + add_suffix(ftmp_data, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i ' + src + ' -d ' + dest + ' -w ' + ','.join(warp_forward) + ' -o ' + add_suffix(src, '_regStep' + str(i_step - 1)) + ' -x ' + interp_step, verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward) + ' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w ' + ','.join(warp_inverse) + ',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i ' + ftmp_data + ' -setorient RPI -o ' + add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i ' + ftmp_seg + ' -setorient RPI -o ' + add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i ' + ftmp_label + ' -setorient RPI -o ' + add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i ' + ftmp_template_label + ' -o ' + ftmp_template_label + ' -remove ' + ftmp_label)
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.setFileName('label_rpi_modif.nii.gz')
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc=param.path_qc)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i ' + src + ' -d ' + dest + ' -w ' + ','.join(warp_forward) + ' -o ' + add_suffix(src, '_regStep' + str(i_step - 1)) + ' -x ' + interp_step, verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run('sct_concat_transfo -w ' + ','.join(warp_forward) + ' -d data.nii -o warp_template2anat.nii.gz', verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run('sct_concat_transfo -w ' + ','.join(warp_inverse) + ' -d template.nii -o warp_anat2template.nii.gz', verbose)
# Apply warping fields to anat and template
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + 'warp_template2anat.nii.gz', path_output + 'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'warp_anat2template.nii.gz', path_output + 'warp_anat2template.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'template2anat.nii.gz', path_output + 'template2anat' + ext_data, verbose)
sct.generate_output_file(path_tmp + 'anat2template.nii.gz', path_output + 'anat2template' + ext_data, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(path_tmp + 'warp_curve2straight.nii.gz', path_output + 'warp_curve2straight.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'warp_straight2curve.nii.gz', path_output + 'warp_straight2curve.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'straight_ref.nii.gz', path_output + 'straight_ref.nii.gz', verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf ' + path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's', verbose)
if '-qc' in arguments and not arguments.get('-noqc', False):
qc_path = arguments['-qc']
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
qc_param = qc.Params(fname_data, 'sct_register_to_template', args, 'Sagittal', qc_path)
report = qc.QcReport(qc_param, '')
@qc.QcImage(report, 'none', [qc.QcImage.no_seg_seg])
def test(qslice):
return qslice.single()
fname_template2anat = path_output + 'template2anat' + ext_data
test(qcslice.SagittalTemplate2Anat(Image(fname_data), Image(fname_template2anat), Image(fname_seg)))
sct.printv('Sucessfully generate the QC results in %s' % qc_param.qc_results)
sct.printv('Use the following command to see the results in a browser')
sct.printv('sct_qc -folder %s' % qc_path, type='info')
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview ' + fname_data + ' ' + path_output + 'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview ' + fname_template + ' -b 0,5000 ' + path_output + 'anat2template &\n', verbose, 'info')
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
gapxy = param.gapxy
gapz = param.gapz
padding = param.padding
centerline_fitting = param.fitting_method
remove_temp_files = param.remove_temp_files
verbose = param.verbose
interpolation_warp = param.interpolation_warp
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
print path_sct
# extract path of the script
path_script = os.path.dirname(__file__) + '/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
# fname_anat = path_sct+'/testing/data/errsm_23/t2/t2.nii.gz'
# fname_centerline = path_sct+'/testing/data/errsm_23/t2/t2_segmentation_PropSeg.nii.gz'
fname_anat = '/home/django/jtouati/data/cover_z_slices/errsm13_t2.nii.gz'
fname_centerline = '/home/django/jtouati/data/cover_z_slices/segmentation_centerline_binary.nii.gz'
remove_temp_files = 0
centerline_fitting = 'splines'
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
verbose = 2
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:c:r:w:f:v:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-w'):
interpolation_warp = str(arg)
elif opt in ('-f'):
centerline_fitting = str(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# Display usage if optional arguments are not correctly provided
if centerline_fitting == '':
centerline_fitting = 'splines'
elif not centerline_fitting == '' and not centerline_fitting == 'splines' and not centerline_fitting == 'polynomial':
print '\n \n -f argument is not valid \n \n'
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# check interp method
if interpolation_warp == 'spline':
interpolation_warp_ants = '--use-BSpline'
elif interpolation_warp == 'trilinear':
interpolation_warp_ants = ''
elif interpolation_warp == 'nearestneighbor':
interpolation_warp_ants = '--use-NN'
else:
print '\WARNING: Interpolation method not recognized. Using: ' + param.interpolation_warp
interpolation_warp_ants = '--use-BSpline'
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ' Centerline fitting option ......... ' + centerline_fitting
print ' Final interpolation ............... ' + interpolation_warp
print ' Verbose ........................... ' + str(verbose)
print ''
# if verbose 2, import matplotlib
if verbose == 2:
import matplotlib.pyplot as plt
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(
fname_centerline)
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.run('cp ' + fname_anat + ' ' + path_tmp)
sct.run('cp ' + fname_centerline + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Open centerline
#==========================================================================================
# Change orientation of the input centerline into RPI
print '\nOrient centerline to RPI orientation...'
fname_centerline_orient = 'tmp.centerline_rpi' + ext_centerline
sct.run('sct_orientation -i ' + file_centerline + ext_centerline + ' -o ' +
fname_centerline_orient + ' -orientation RPI')
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_centerline_orient)
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
print '\nOpen centerline volume...'
file = nibabel.load(fname_centerline_orient)
data = file.get_data()
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(0, nz, 1)]
y_centerline = [0 for iz in range(0, nz, 1)]
z_centerline = [iz for iz in range(0, nz, 1)]
x_centerline_deriv = [0 for iz in range(0, nz, 1)]
y_centerline_deriv = [0 for iz in range(0, nz, 1)]
z_centerline_deriv = [0 for iz in range(0, nz, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slice contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
#
# x_seg_start, y_seg_start = (data[:,:,0]>0).nonzero()
# x_seg_end, y_seg_end = (data[:,:,-1]>0).nonzero()
# REMOVED: 2014-07-18
# check if centerline covers all the image
# if len(x_seg_start)==0 or len(x_seg_end)==0:
# print '\nERROR: centerline/segmentation must cover all "z" slices of the input image.\n' \
# 'To solve the problem, you need to crop the input image (you can use \'sct_crop_image\') and generate one' \
# 'more time the spinal cord centerline/segmentation from this cropped image.\n'
# usage()
#
# X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
# if (len(X) > 0): # Scenario 1
# for iz in range(0, nz, 1):
# x_centerline[iz], y_centerline[iz] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
# else: # Scenario 2
# for iz in range(0, nz, 1):
# print (data[:,:,iz]>0).nonzero()
# x_seg, y_seg = (data[:,:,iz]>0).nonzero()
# x_centerline[iz] = numpy.mean(x_seg)
# y_centerline[iz] = numpy.mean(y_seg)
# # TODO: find a way to do the previous loop with this, which is more neat:
# # [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# get center of mass of the centerline/segmentation
print '\nGet center of mass of the centerline/segmentation...'
for iz in range(0, nz, 1):
x_centerline[iz], y_centerline[
iz] = ndimage.measurements.center_of_mass(
numpy.array(data[:, :, iz]))
#print len(x_centerline),len(y_centerline)
#print len((numpy.array(x_centerline)>=0).nonzero()[0]),len((numpy.array(y_centerline)>=0).nonzero()[0])
x_seg_start, y_seg_start = (data[:, :, 0] > 0).nonzero()
x_seg_end, y_seg_end = (data[:, :, -1] > 0).nonzero()
#check if centerline covers all the image
if len(x_seg_start) == 0 or len(x_seg_end) == 0:
sct.printv(
'\nWARNING : the centerline/segmentation you gave does not cover all "z" slices of the input image. Results should be improved if you crop the input image (you can use \'sct_crop_image\') and generate a new spinalcord centerline/segmentation from this cropped image.\n',
1, 'warning')
# print '\nWARNING : the centerline/segmentation you gave does not cover all "z" slices of the input image.\n' \
# 'Results should be improved if you crop the input image (you can use \'sct_crop_image\') and generate\n'\
# 'a new spinalcord centerline/segmentation from this cropped image.\n'
#print len((numpy.array(x_centerline)>=0).nonzero()[0]),len((numpy.array(y_centerline)>=0).nonzero()[0])
min_centerline = min((numpy.array(x_centerline) >= 0).nonzero()[0])
max_centerline = max((numpy.array(x_centerline) >= 0).nonzero()[0])
z_centerline = z_centerline[(min_centerline):(max_centerline + 1)]
#print len(z_centerline)
nz = len(z_centerline)
x_centerline = [x for x in x_centerline if not isnan(x)]
y_centerline = [y for y in y_centerline if not isnan(y)]
#print len(x_centerline),len(y_centerline)
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new fitted coordinates
if centerline_fitting == 'splines':
x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = msct_smooth.b_spline_nurbs(
x_centerline, y_centerline, z_centerline)
#x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
elif centerline_fitting == 'polynomial':
x_centerline_fit, y_centerline_fit, polyx, polyy = polynome_centerline(
x_centerline, y_centerline, z_centerline)
#numpy.interp([i for i in xrange(0,min_centerline+1)],
#y_centerline_fit
#print z_centerline
if verbose == 2:
# plot centerline
ax = plt.subplot(1, 2, 1)
plt.plot(x_centerline, z_centerline, 'b:', label='centerline')
plt.plot(x_centerline_fit, z_centerline, 'r-', label='fit')
plt.xlabel('x')
plt.ylabel('z')
ax = plt.subplot(1, 2, 2)
plt.plot(y_centerline, z_centerline, 'b:', label='centerline')
plt.plot(y_centerline_fit, z_centerline, 'r-', label='fit')
plt.xlabel('y')
plt.ylabel('z')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels)
plt.show()
# Get coordinates of landmarks along curved centerline
#==========================================================================================
print '\nGet coordinates of landmarks along curved centerline...'
# landmarks are created along the curved centerline every z=gapz. They consist of a "cross" of size gapx and gapy.
# find derivative of polynomial
step_z = round(nz / gapz)
#iz_curved = [i for i in range (0, nz, gapz)]
iz_curved = [(min(z_centerline) + i * step_z) for i in range(0, gapz)]
iz_curved.append(max(z_centerline))
#print iz_curved, len(iz_curved)
n_iz_curved = len(iz_curved)
#print n_iz_curved
landmark_curved = [[[0 for i in range(0, 3)] for i in range(0, 5)]
for i in iz_curved]
# print x_centerline_deriv,len(x_centerline_deriv)
# landmark[a][b][c]
# a: index along z. E.g., the first cross with have index=0, the next index=1, and so on...
# b: index of element on the cross. I.e., 0: center of the cross, 1: +x, 2 -x, 3: +y, 4: -y
# c: dimension, i.e., 0: x, 1: y, 2: z
# loop across index, which corresponds to iz (points along the centerline)
if centerline_fitting == 'polynomial':
for index in range(0, n_iz_curved, 1):
# set coordinates for landmark at the center of the cross
landmark_curved[index][0][0], landmark_curved[index][0][
1], landmark_curved[index][0][2] = x_centerline_fit[
iz_curved[index]], y_centerline_fit[
iz_curved[index]], iz_curved[index]
# set x and z coordinates for landmarks +x and -x
landmark_curved[index][1][2], landmark_curved[index][1][
0], landmark_curved[index][2][2], landmark_curved[index][2][
0] = get_points_perpendicular_to_curve(
polyx, polyx.deriv(), iz_curved[index], gapxy)
# set y coordinate to y_centerline_fit[iz] for elements 1 and 2 of the cross
for i in range(1, 3):
landmark_curved[index][i][1] = y_centerline_fit[
iz_curved[index]]
# set coordinates for landmarks +y and -y. Here, x coordinate is 0 (already initialized).
landmark_curved[index][3][2], landmark_curved[index][3][
1], landmark_curved[index][4][2], landmark_curved[index][4][
1] = get_points_perpendicular_to_curve(
polyy, polyy.deriv(), iz_curved[index], gapxy)
# set x coordinate to x_centerline_fit[iz] for elements 3 and 4 of the cross
for i in range(3, 5):
landmark_curved[index][i][0] = x_centerline_fit[
iz_curved[index]]
elif centerline_fitting == 'splines':
for index in range(0, n_iz_curved, 1):
# calculate d (ax+by+cz+d=0)
# print iz_curved[index]
a = x_centerline_deriv[iz_curved[index] - min(z_centerline)]
b = y_centerline_deriv[iz_curved[index] - min(z_centerline)]
c = z_centerline_deriv[iz_curved[index] - min(z_centerline)]
x = x_centerline_fit[iz_curved[index] - min(z_centerline)]
y = y_centerline_fit[iz_curved[index] - min(z_centerline)]
z = iz_curved[index]
d = -(a * x + b * y + c * z)
#print a,b,c,d,x,y,z
# set coordinates for landmark at the center of the cross
landmark_curved[index][0][0], landmark_curved[index][0][
1], landmark_curved[index][0][2] = x_centerline_fit[
iz_curved[index] - min(z_centerline)], y_centerline_fit[
iz_curved[index] - min(z_centerline)], iz_curved[index]
# set y coordinate to y_centerline_fit[iz] for elements 1 and 2 of the cross
for i in range(1, 3):
landmark_curved[index][i][1] = y_centerline_fit[
iz_curved[index] - min(z_centerline)]
# set x and z coordinates for landmarks +x and -x, forcing de landmark to be in the orthogonal plan and the distance landmark/curve to be gapxy
x_n = Symbol('x_n')
landmark_curved[index][2][0], landmark_curved[index][1][0] = solve(
(x_n - x)**2 + ((-1 / c) * (a * x_n + b * y + d) - z)**2 -
gapxy**2, x_n) #x for -x and +x
landmark_curved[index][1][2] = (-1 / c) * (
a * landmark_curved[index][1][0] + b * y + d) #z for +x
landmark_curved[index][2][2] = (-1 / c) * (
a * landmark_curved[index][2][0] + b * y + d) #z for -x
# set x coordinate to x_centerline_fit[iz] for elements 3 and 4 of the cross
for i in range(3, 5):
landmark_curved[index][i][0] = x_centerline_fit[
iz_curved[index] - min(z_centerline)]
# set coordinates for landmarks +y and -y. Here, x coordinate is 0 (already initialized).
y_n = Symbol('y_n')
landmark_curved[index][4][1], landmark_curved[index][3][1] = solve(
(y_n - y)**2 + ((-1 / c) * (a * x + b * y_n + d) - z)**2 -
gapxy**2, y_n) #y for -y and +y
landmark_curved[index][3][2] = (-1 / c) * (
a * x + b * landmark_curved[index][3][1] + d) #z for +y
landmark_curved[index][4][2] = (-1 / c) * (
a * x + b * landmark_curved[index][4][1] + d) #z for -y
# #display
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.plot(x_centerline_fit, y_centerline_fit,z_centerline, 'g')
# ax.plot(x_centerline, y_centerline,z_centerline, 'r')
# ax.plot([landmark_curved[i][j][0] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_curved[i][j][1] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_curved[i][j][2] for i in range(0, n_iz_curved) for j in range(0, 5)], '.')
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_zlabel('z')
# plt.show()
# Get coordinates of landmarks along straight centerline
#==========================================================================================
print '\nGet coordinates of landmarks along straight centerline...'
landmark_straight = [[[0 for i in range(0, 3)] for i in range(0, 5)]
for i in iz_curved
] # same structure as landmark_curved
# calculate the z indices corresponding to the Euclidean distance between two consecutive points on the curved centerline (approximation curve --> line)
iz_straight = [(min(z_centerline) + 0) for i in range(0, gapz + 1)]
#print iz_straight,len(iz_straight)
for index in range(1, n_iz_curved, 1):
# compute vector between two consecutive points on the curved centerline
vector_centerline = [x_centerline_fit[iz_curved[index]-min(z_centerline)] - x_centerline_fit[iz_curved[index-1]-min(z_centerline)], \
y_centerline_fit[iz_curved[index]-min(z_centerline)] - y_centerline_fit[iz_curved[index-1]-min(z_centerline)], \
iz_curved[index] - iz_curved[index-1]]
# compute norm of this vector
norm_vector_centerline = numpy.linalg.norm(vector_centerline, ord=2)
# round to closest integer value
norm_vector_centerline_rounded = int(round(norm_vector_centerline, 0))
# assign this value to the current z-coordinate on the straight centerline
iz_straight[index] = iz_straight[index -
1] + norm_vector_centerline_rounded
# initialize x0 and y0 to be at the center of the FOV
x0 = int(round(nx / 2))
y0 = int(round(ny / 2))
for index in range(0, n_iz_curved, 1):
# set coordinates for landmark at the center of the cross
landmark_straight[index][0][0], landmark_straight[index][0][
1], landmark_straight[index][0][2] = x0, y0, iz_straight[index]
# set x, y and z coordinates for landmarks +x
landmark_straight[index][1][0], landmark_straight[index][1][
1], landmark_straight[index][1][2] = x0 + gapxy, y0, iz_straight[
index]
# set x, y and z coordinates for landmarks -x
landmark_straight[index][2][0], landmark_straight[index][2][
1], landmark_straight[index][2][2] = x0 - gapxy, y0, iz_straight[
index]
# set x, y and z coordinates for landmarks +y
landmark_straight[index][3][0], landmark_straight[index][3][
1], landmark_straight[index][3][2] = x0, y0 + gapxy, iz_straight[
index]
# set x, y and z coordinates for landmarks -y
landmark_straight[index][4][0], landmark_straight[index][4][
1], landmark_straight[index][4][2] = x0, y0 - gapxy, iz_straight[
index]
# # display
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# #ax.plot(x_centerline_fit, y_centerline_fit,z_centerline, 'r')
# ax.plot([landmark_straight[i][j][0] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_straight[i][j][1] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_straight[i][j][2] for i in range(0, n_iz_curved) for j in range(0, 5)], '.')
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_zlabel('z')
# plt.show()
#
# Create NIFTI volumes with landmarks
#==========================================================================================
# Pad input volume to deal with the fact that some landmarks on the curved centerline might be outside the FOV
# N.B. IT IS VERY IMPORTANT TO PAD ALSO ALONG X and Y, OTHERWISE SOME LANDMARKS MIGHT GET OUT OF THE FOV!!!
print '\nPad input volume to deal with the fact that some landmarks on the curved centerline might be outside the FOV...'
sct.run('isct_c3d ' + fname_centerline_orient + ' -pad ' + str(padding) +
'x' + str(padding) + 'x' + str(padding) + 'vox ' + str(padding) +
'x' + str(padding) + 'x' + str(padding) +
'vox 0 -o tmp.centerline_pad.nii.gz')
# TODO: don't pad input volume: no need for that! instead, try to increase size of hdr when saving landmarks.
# Open padded centerline for reading
print '\nOpen padded centerline for reading...'
file = nibabel.load('tmp.centerline_pad.nii.gz')
data = file.get_data()
hdr = file.get_header()
# Create volumes containing curved and straight landmarks
data_curved_landmarks = data * 0
data_straight_landmarks = data * 0
# initialize landmark value
landmark_value = 1
# Loop across cross index
for index in range(0, n_iz_curved, 1):
# loop across cross element index
for i_element in range(0, 5, 1):
# get x, y and z coordinates of curved landmark (rounded to closest integer)
x, y, z = int(round(landmark_curved[index][i_element][0])), int(
round(landmark_curved[index][i_element][1])), int(
round(landmark_curved[index][i_element][2]))
# attribute landmark_value to the voxel and its neighbours
data_curved_landmarks[x + padding - 1:x + padding + 2,
y + padding - 1:y + padding + 2, z +
padding - 1:z + padding + 2] = landmark_value
# get x, y and z coordinates of straight landmark (rounded to closest integer)
x, y, z = int(round(landmark_straight[index][i_element][0])), int(
round(landmark_straight[index][i_element][1])), int(
round(landmark_straight[index][i_element][2]))
# attribute landmark_value to the voxel and its neighbours
data_straight_landmarks[x + padding - 1:x + padding + 2,
y + padding - 1:y + padding + 2,
z + padding - 1:z + padding +
2] = landmark_value
# increment landmark value
landmark_value = landmark_value + 1
# Write NIFTI volumes
hdr.set_data_dtype(
'uint32') # set imagetype to uint8 #TODO: maybe use int32
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(data_curved_landmarks, None, hdr)
nibabel.save(img, 'tmp.landmarks_curved.nii.gz')
print '.. File created: tmp.landmarks_curved.nii.gz'
img = nibabel.Nifti1Image(data_straight_landmarks, None, hdr)
nibabel.save(img, 'tmp.landmarks_straight.nii.gz')
print '.. File created: tmp.landmarks_straight.nii.gz'
# Estimate deformation field by pairing landmarks
#==========================================================================================
# Dilate landmarks (because nearest neighbour interpolation will be later used, therefore some landmarks may "disapear" if they are single points)
#print '\nDilate landmarks...'
#sct.run(fsloutput+'fslmaths tmp.landmarks_curved.nii -kernel box 3x3x3 -dilD tmp.landmarks_curved_dilated -odt short')
#sct.run(fsloutput+'fslmaths tmp.landmarks_straight.nii -kernel box 3x3x3 -dilD tmp.landmarks_straight_dilated -odt short')
# Estimate rigid transformation
print '\nEstimate rigid transformation between paired landmarks...'
sct.run(
'isct_ANTSUseLandmarkImagesToGetAffineTransform tmp.landmarks_straight.nii.gz tmp.landmarks_curved.nii.gz rigid tmp.curve2straight_rigid.txt'
)
# Apply rigid transformation
print '\nApply rigid transformation to curved landmarks...'
sct.run(
'sct_WarpImageMultiTransform 3 tmp.landmarks_curved.nii.gz tmp.landmarks_curved_rigid.nii.gz -R tmp.landmarks_straight.nii.gz tmp.curve2straight_rigid.txt --use-NN'
)
# Estimate b-spline transformation curve --> straight
print '\nEstimate b-spline transformation: curve --> straight...'
sct.run(
'isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField tmp.landmarks_straight.nii.gz tmp.landmarks_curved_rigid.nii.gz tmp.warp_curve2straight.nii.gz 5x5x5 3 2 0'
)
# Concatenate rigid and non-linear transformations...
print '\nConcatenate rigid and non-linear transformations...'
#sct.run('isct_ComposeMultiTransform 3 tmp.warp_rigid.nii -R tmp.landmarks_straight.nii tmp.warp.nii tmp.curve2straight_rigid.txt')
# TODO: use sct.run() when output from the following command will be different from 0 (currently there seem to be a bug)
cmd = 'isct_ComposeMultiTransform 3 tmp.curve2straight.nii.gz -R tmp.landmarks_straight.nii.gz tmp.warp_curve2straight.nii.gz tmp.curve2straight_rigid.txt'
print('>> ' + cmd)
commands.getstatusoutput(cmd)
# Estimate b-spline transformation straight --> curve
# TODO: invert warping field instead of estimating a new one
print '\nEstimate b-spline transformation: straight --> curve...'
sct.run(
'isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField tmp.landmarks_curved_rigid.nii.gz tmp.landmarks_straight.nii.gz tmp.warp_straight2curve.nii.gz 5x5x5 3 2 0'
)
# Concatenate rigid and non-linear transformations...
print '\nConcatenate rigid and non-linear transformations...'
#sct.run('isct_ComposeMultiTransform 3 tmp.warp_rigid.nii -R tmp.landmarks_straight.nii tmp.warp.nii tmp.curve2straight_rigid.txt')
# TODO: use sct.run() when output from the following command will be different from 0 (currently there seem to be a bug)
cmd = 'isct_ComposeMultiTransform 3 tmp.straight2curve.nii.gz -R tmp.landmarks_straight.nii.gz -i tmp.curve2straight_rigid.txt tmp.warp_straight2curve.nii.gz'
print('>> ' + cmd)
commands.getstatusoutput(cmd)
#print '\nPad input image...'
#sct.run('isct_c3d '+fname_anat+' -pad '+str(padz)+'x'+str(padz)+'x'+str(padz)+'vox '+str(padz)+'x'+str(padz)+'x'+str(padz)+'vox 0 -o tmp.anat_pad.nii')
# Unpad landmarks...
# THIS WAS REMOVED ON 2014-06-03 because the output data was cropped at the edge, which caused landmarks to sometimes disappear
# print '\nUnpad landmarks...'
# sct.run('fslroi tmp.landmarks_straight.nii.gz tmp.landmarks_straight_crop.nii.gz '+str(padding)+' '+str(nx)+' '+str(padding)+' '+str(ny)+' '+str(padding)+' '+str(nz))
# Apply deformation to input image
print '\nApply transformation to input image...'
sct.run('sct_WarpImageMultiTransform 3 ' + file_anat + ext_anat +
' tmp.anat_rigid_warp.nii.gz -R tmp.landmarks_straight.nii.gz ' +
interpolation_warp + ' tmp.curve2straight.nii.gz')
# sct.run('sct_WarpImageMultiTransform 3 '+fname_anat+' tmp.anat_rigid_warp.nii.gz -R tmp.landmarks_straight_crop.nii.gz '+interpolation_warp+ ' tmp.curve2straight.nii.gz')
# come back to parent folder
os.chdir('..')
# Generate output file (in current folder)
# TODO: do not uncompress the warping field, it is too time consuming!
print '\nGenerate output file (in current folder)...'
sct.generate_output_file(path_tmp + '/tmp.curve2straight.nii.gz', '',
'warp_curve2straight', '.nii.gz') # warping field
sct.generate_output_file(path_tmp + '/tmp.straight2curve.nii.gz', '',
'warp_straight2curve', '.nii.gz') # warping field
sct.generate_output_file(path_tmp + '/tmp.anat_rigid_warp.nii.gz', '',
file_anat + '_straight',
ext_anat) # straightened anatomic
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
print '\nDone!\n'
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
remove_temp_files = param.remove_temp_files
verbose = param.verbose
start_time = time.time()
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:c:r:s:v:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-r'):
remove_temp_files = arg
elif opt in ('-s'):
sigma = arg
elif opt in ('-v'):
verbose = int(arg)
# Display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# Display arguments
print '\nCheck input arguments...'
print ' Volume to smooth .................. ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ' FWHM .............................. '+str(sigma)
print ' Verbose ........................... '+str(verbose)
# Check existence of input files
print('\nCheck existence of input files...')
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(fname_centerline)
# create temporary folder
print('\nCreate temporary folder...')
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# copy files to temporary folder
print('\nCopy files...')
sct.run('c3d '+fname_anat+' -o '+path_tmp+'/anat.nii')
sct.run('c3d '+fname_centerline+' -o '+path_tmp+'/centerline.nii')
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input image into RPI
print '\nOrient input volume to RPI orientation...'
sct.run('sct_orientation -i anat.nii -o anat_rpi.nii -orientation RPI')
# Change orientation of the input image into RPI
print '\nOrient centerline to RPI orientation...'
sct.run('sct_orientation -i centerline.nii -o centerline_rpi.nii -orientation RPI')
# Straighten the spinal cord
print '\nStraighten the spinal cord...'
sct.run('sct_straighten_spinalcord.py -i anat_rpi.nii -c centerline_rpi.nii -w spline -v '+str(verbose))
# Smooth the straightened image along z
print '\nSmooth the straightened image along z...'
sct.run('c3d anat_rpi_straight.nii -smooth 0x0x'+str(sigma)+'vox -o anat_rpi_straight_smooth.nii')
# Apply the reversed warping field to get back the curved spinal cord
print '\nApply the reversed warping field to get back the curved spinal cord (assuming a 3D image)...'
sct.run('WarpImageMultiTransform 3 anat_rpi_straight_smooth.nii anat_rpi_straight_smooth_curved.nii -R anat.nii --use-BSpline warp_straight2curve.nii.gz')
# come back to parent folder
os.chdir('..')
# Generate output file
print '\nGenerate output file...'
sct.generate_output_file(path_tmp+'/anat_rpi_straight_smooth_curved.nii','',file_anat+'_smooth',ext_anat)
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
#Display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s\n'
# to view results
print 'To view results, type:'
print 'fslview '+file_anat+' '+file_anat+'_smooth &\n'
def main():
parser = get_parser()
param = Param()
arguments = parser.parse(sys.argv[1:])
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
path_template = sct.slash_at_the_end(arguments['-t'], 1)
contrast_template = arguments['-c']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
if 'cpu-nb' in arguments:
arg_cpu = ' -cpu-nb '+arguments['-cpu-nb']
else:
arg_cpu = ''
if '-param' in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
# initialize other parameters
file_template_label = param.file_template_label
output_type = param.output_type
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# capitalize letters for contrast
if contrast_template == 't1':
contrast_template = 'T1'
elif contrast_template == 't2':
contrast_template = 'T2'
# retrieve file_template based on contrast
fname_template_list = glob(path_template+param.folder_template+'*'+contrast_template+'.nii.gz')
# TODO: make sure there is only one file -- check if file is there otherwise it crashes
fname_template = fname_template_list[0]
# retrieve file_template_seg
fname_template_seg_list = glob(path_template+param.folder_template+'*cord.nii.gz')
# TODO: make sure there is only one file
fname_template_seg = fname_template_seg_list[0]
# start timer
start_time = time.time()
# get absolute path - TO DO: remove! NEVER USE ABSOLUTE PATH...
path_template = os.path.abspath(path_template+param.folder_template)
# get fname of the template + template objects
# fname_template = sct.slash_at_the_end(path_template, 1)+file_template
fname_template_label = sct.slash_at_the_end(path_template, 1)+file_template_label
# fname_template_seg = sct.slash_at_the_end(path_template, 1)+file_template_seg
# check file existence
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_label, verbose)
sct.check_file_exist(fname_template_seg, verbose)
# print arguments
sct.printv('\nCheck parameters:', verbose)
sct.printv('.. Data: '+fname_data, verbose)
sct.printv('.. Landmarks: '+fname_landmarks, verbose)
sct.printv('.. Segmentation: '+fname_seg, verbose)
sct.printv('.. Path template: '+path_template, verbose)
sct.printv('.. Path output: '+path_output, verbose)
sct.printv('.. Output type: '+str(output_type), verbose)
sct.printv('.. Remove temp files: '+str(remove_temp_files), verbose)
sct.printv('\nParameters for registration:')
for pStep in range(1, len(paramreg.steps)+1):
sct.printv('Step #'+paramreg.steps[str(pStep)].step, verbose)
sct.printv('.. Type #'+paramreg.steps[str(pStep)].type, verbose)
sct.printv('.. Algorithm................ '+paramreg.steps[str(pStep)].algo, verbose)
sct.printv('.. Metric................... '+paramreg.steps[str(pStep)].metric, verbose)
sct.printv('.. Number of iterations..... '+paramreg.steps[str(pStep)].iter, verbose)
sct.printv('.. Shrink factor............ '+paramreg.steps[str(pStep)].shrink, verbose)
sct.printv('.. Smoothing factor......... '+paramreg.steps[str(pStep)].smooth, verbose)
sct.printv('.. Gradient step............ '+paramreg.steps[str(pStep)].gradStep, verbose)
sct.printv('.. Degree of polynomial..... '+paramreg.steps[str(pStep)].poly, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
sct.printv('\nCheck input labels...')
# check if label image contains coherent labels
image_label = Image(fname_landmarks)
# -> all labels must be different
labels = image_label.getNonZeroCoordinates(sorting='value')
hasDifferentLabels = True
for lab in labels:
for otherlabel in labels:
if lab != otherlabel and lab.hasEqualValue(otherlabel):
hasDifferentLabels = False
break
if not hasDifferentLabels:
sct.printv('ERROR: Wrong landmarks input. All labels must be different.', verbose, 'error')
# all labels must be available in tempalte
image_label_template = Image(fname_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# create temporary folder
path_tmp = sct.tmp_create(verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i '+fname_data+' -o '+path_tmp+ftmp_data)
sct.run('sct_convert -i '+fname_seg+' -o '+path_tmp+ftmp_seg)
sct.run('sct_convert -i '+fname_landmarks+' -o '+path_tmp+ftmp_label)
sct.run('sct_convert -i '+fname_template+' -o '+path_tmp+ftmp_template)
sct.run('sct_convert -i '+fname_template_seg+' -o '+path_tmp+ftmp_template_seg)
sct.run('sct_convert -i '+fname_template_label+' -o '+path_tmp+ftmp_template_label)
# go to tmp folder
os.chdir(path_tmp)
# smooth segmentation (jcohenadad, issue #613)
sct.printv('\nSmooth segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i '+ftmp_data+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i '+ftmp_seg+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i '+ftmp_data+' -setorient RPI -o '+add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i '+ftmp_seg+' -setorient RPI -o '+add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i '+ftmp_label+' -setorient RPI -o '+add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
sct.run('sct_straighten_spinalcord -i '+ftmp_seg+' -s '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straight')+' -qc 0 -r 0 -v '+str(verbose)+' '+param.param_straighten+arg_cpu, verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d '+ftmp_data+' -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -p remove -i '+ftmp_template_label+' -o '+ftmp_template_label+' -r '+ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_dilate')+' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_straight')+' -d '+add_suffix(ftmp_seg, '_straight')+' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Create crosses for the template labels and get coordinates
sct.printv('\nCreate a 15 mm cross for the template labels...', verbose)
template_image = Image(ftmp_template_label)
coordinates_input = template_image.getNonZeroCoordinates(sorting='value')
# jcohenadad, issue #628 <<<<<
# landmark_template = ProcessLabels.get_crosses_coordinates(coordinates_input, gapxy=15)
landmark_template = coordinates_input
# >>>>>
if verbose == 2:
# TODO: assign cross to image before saving
template_image.setFileName(add_suffix(ftmp_template_label, '_cross'))
template_image.save(type='minimize_int')
# Create crosses for the input labels into straight space and get coordinates
sct.printv('\nCreate a 15 mm cross for the input labels...', verbose)
label_straight_image = Image(ftmp_label)
coordinates_input = label_straight_image.getCoordinatesAveragedByValue() # landmarks are sorted by value
# jcohenadad, issue #628 <<<<<
# landmark_straight = ProcessLabels.get_crosses_coordinates(coordinates_input, gapxy=15)
landmark_straight = coordinates_input
# >>>>>
if verbose == 2:
# TODO: assign cross to image before saving
label_straight_image.setFileName(add_suffix(ftmp_label, '_cross'))
label_straight_image.save(type='minimize_int')
# Reorganize landmarks
points_fixed, points_moving = [], []
for coord in landmark_straight:
point_straight = label_straight_image.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_moving.append([point_straight[0][0], point_straight[0][1], point_straight[0][2]])
for coord in landmark_template:
point_template = template_image.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_fixed.append([point_template[0][0], point_template[0][1], point_template[0][2]])
# Register curved landmarks on straight landmarks based on python implementation
sct.printv('\nComputing rigid transformation (algo=translation-scaling-z) ...', verbose)
import msct_register_landmarks
# for some reason, the moving and fixed points are inverted between ITK transform and our python-based transform.
# and for another unknown reason, x and y dimensions have a negative sign (at least for translation and center of rotation).
if verbose == 2:
show_transfo = True
else:
show_transfo = False
(rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = msct_register_landmarks.getRigidTransformFromLandmarks(points_moving, points_fixed, constraints='translation-scaling-z', show=show_transfo)
# writing rigid transformation file
text_file = open("straight2templateAffine.txt", "w")
text_file.write("#Insight Transform File V1.0\n")
text_file.write("#Transform 0\n")
text_file.write("Transform: AffineTransform_double_3_3\n")
text_file.write("Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\n" % (
rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],
rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],
rotation_matrix[2, 0], rotation_matrix[2, 1], rotation_matrix[2, 2],
-translation_array[0, 0], -translation_array[0, 1], translation_array[0, 2]))
text_file.write("FixedParameters: %.9f %.9f %.9f\n" % (-points_moving_barycenter[0],
-points_moving_barycenter[1],
points_moving_barycenter[2]))
text_file.close()
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
# threshold and binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -thr 0.4 -o '+add_suffix(ftmp_seg, '_thr'))
sct.run('sct_maths -i '+add_suffix(ftmp_seg, '_thr')+' -bin -o '+add_suffix(ftmp_seg, '_thr_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_thr_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)+1):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+add_suffix(src, '_reg')+' -x '+interp_step, verbose)
src = add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# Apply warping fields to anat and template
if output_type == 1:
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'warp_template2anat.nii.gz', path_output+'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_anat2template.nii.gz', path_output+'warp_anat2template.nii.gz', verbose)
if output_type == 1:
sct.generate_output_file(path_tmp+'template2anat.nii.gz', path_output+'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'anat2template.nii.gz', path_output+'anat2template'+ext_data, verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_data+' '+path_output+'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 '+path_output+'anat2template &\n', verbose, 'info')
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix+file_data+ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. '+orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp+'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp+'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0', verbose=False)
if method_type == 'centerline':
sct.run('sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0', verbose=False)
if method_type == 'point':
sct.run('sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0', verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in '+os.path.basename(__file__)+': Input image is 4d but output mask will 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run('sct_label_utils -i point_RPI.nii.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, even=param.even, spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run('sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' + orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
def main():
# Initialization
fname_data = ''
fname_landmarks = ''
fname_seg = ''
folder_template = param.folder_template
file_template = param.file_template
file_template_label = param.file_template_label
file_template_seg = param.file_template_seg
output_type = param.output_type
speed = param.speed
remove_temp_files = param.remove_temp_files
verbose = param.verbose
smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# # get path of the template
# path_template = path_sct+folder_template
# get fname of the template + template objects
fname_template = path_sct+folder_template+'/'+file_template
fname_template_label = path_sct+folder_template+'/'+file_template_label
fname_template_seg = path_sct+folder_template+'/'+file_template_seg
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = path_sct+'/testing/data/errsm_23/t2/t2.nii.gz'
fname_landmarks = path_sct+'/testing/data/errsm_23/t2/t2_landmarks_C2_T2_center.nii.gz'
fname_seg = path_sct+'/testing/data/errsm_23/t2/t2_segmentation_PropSeg.nii.gz'
speed = 'superfast'
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:l:m:o:r:s:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
fname_data = arg
elif opt in ('-l'):
fname_landmarks = arg
elif opt in ("-m"):
fname_seg = arg
elif opt in ("-o"):
output_type = int(arg)
elif opt in ("-r"):
remove_temp_files = int(arg)
elif opt in ("-s"):
speed = arg
# display usage if a mandatory argument is not provided
if fname_data == '' or fname_landmarks == '' or fname_seg == '':
usage()
# print arguments
print '\nCheck parameters:'
print '.. Data: '+fname_data
print '.. Landmarks: '+fname_landmarks
print '.. Segmentation: '+fname_seg
print '.. Output type: '+str(output_type)
print '.. Speed: '+speed
print '.. Remove temp files: '+str(remove_temp_files)
# Check speed parameter and create registration mode: slow 50x30, normal 50x15, fast 10x3 (default)
print('\nAssign number of iterations based on speed...')
if speed == "slow":
nb_iterations = "50x30"
elif speed == "normal":
nb_iterations = "50x15"
elif speed == "fast":
nb_iterations = "10x3"
elif speed == "superfast":
nb_iterations = "3x1" # only for debugging purpose-- do not inform the user about this option
else:
print 'ERROR: Wrong input registration speed {slow, normal, fast}.'
sys.exit(2)
print '.. '+nb_iterations
# Get full path
# fname_data = os.path.abspath(fname_data)
# fname_landmarks = os.path.abspath(fname_landmarks)
# fname_seg = os.path.abspath(fname_seg)
# check existence of input files
print('\nCheck existence of input files...')
sct.check_file_exist(fname_data,verbose)
sct.check_file_exist(fname_landmarks,verbose)
sct.check_file_exist(fname_seg,verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# create temporary folder
print('\nCreate temporary folder...')
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
status, output = sct.run('mkdir '+path_tmp)
# copy files to temporary folder
print('\nCopy files...')
status, output = sct.run('c3d '+fname_data+' -o '+path_tmp+'/data.nii')
status, output = sct.run('c3d '+fname_landmarks+' -o '+path_tmp+'/landmarks.nii.gz')
status, output = sct.run('c3d '+fname_seg+' -o '+path_tmp+'/segmentation.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of input images to RPI
print('\nChange orientation of input images to RPI...')
status, output = sct.run('sct_orientation -i data.nii -o data_rpi.nii -orientation RPI')
status, output = sct.run('sct_orientation -i landmarks.nii.gz -o landmarks_rpi.nii.gz -orientation RPI')
status, output = sct.run('sct_orientation -i segmentation.nii.gz -o segmentation_rpi.nii.gz -orientation RPI')
# Straighten the spinal cord using centerline/segmentation
print('\nStraighten the spinal cord using centerline/segmentation...')
status, output = sct.run('sct_straighten_spinalcord.py -i data_rpi.nii -c segmentation_rpi.nii.gz -r '+str(remove_temp_files))
# Apply straightening to segmentation
print('\nApply straightening to segmentation...')
sct.run('WarpImageMultiTransform 3 segmentation_rpi.nii.gz segmentation_rpi_straight.nii.gz -R data_rpi_straight.nii warp_curve2straight.nii.gz')
# Smoothing along centerline to improve accuracy and remove step effects
print('\nSmoothing along centerline to improve accuracy and remove step effects...')
sct.run('c3d data_rpi_straight.nii -smooth 0x0x'+str(smoothing_sigma)+'vox -o data_rpi_straight.nii')
sct.run('c3d segmentation_rpi_straight.nii.gz -smooth 0x0x'+str(smoothing_sigma)+'vox -o segmentation_rpi_straight.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
print('\nRemove unused label on template. Keep only label present in the input label image...')
status, output = sct.run('sct_label_utils.py -t remove -i '+fname_template_label+' -o template_label.nii.gz -r landmarks_rpi.nii.gz')
# Create a cross for the template labels - 5 mm
print('\nCreate a 5 mm cross for the template labels...')
status, output = sct.run('sct_label_utils.py -t cross -i template_label.nii.gz -o template_label_cross.nii.gz -c 5')
# Create a cross for the input labels and dilate for straightening preparation - 5 mm
print('\nCreate a 5mm cross for the input labels and dilate for straightening preparation...')
status, output = sct.run('sct_label_utils.py -t cross -i landmarks_rpi.nii.gz -o landmarks_rpi_cross3x3.nii.gz -c 5 -d')
# Push the input labels in the template space
print('\nPush the input labels to the straight space...')
status, output = sct.run('WarpImageMultiTransform 3 landmarks_rpi_cross3x3.nii.gz landmarks_rpi_cross3x3_straight.nii.gz -R data_rpi_straight.nii warp_curve2straight.nii.gz --use-NN')
# Convert landmarks from FLOAT32 to INT
print '\nConvert landmarks from FLOAT32 to INT...'
sct.run('c3d landmarks_rpi_cross3x3_straight.nii.gz -type int -o landmarks_rpi_cross3x3_straight.nii.gz')
# Estimate affine transfo: straight --> template (landmark-based)'
print '\nEstimate affine transfo: straight anat --> template (landmark-based)...'
sct.run('ANTSUseLandmarkImagesToGetAffineTransform template_label_cross.nii.gz landmarks_rpi_cross3x3_straight.nii.gz affine straight2templateAffine.txt')
# Apply affine transformation: straight --> template
print '\nApply affine transformation: straight --> template...'
sct.run('WarpImageMultiTransform 3 data_rpi_straight.nii data_rpi_straight2templateAffine.nii straight2templateAffine.txt -R '+fname_template)
sct.run('WarpImageMultiTransform 3 segmentation_rpi_straight.nii.gz segmentation_rpi_straight2templateAffine.nii.gz straight2templateAffine.txt -R '+fname_template)
# now threshold at 0.5 (for partial volume interpolation)
# do not do that anymore-- better to estimate transformation using trilinear interp image to avoid step effect. See issue #31 on github.
# sct.run('c3d segmentation_rpi_straight2templateAffine.nii.gz -threshold -inf 0.5 0 1 -o segmentation_rpi_straight2templateAffine.nii.gz')
# Registration straight spinal cord to template
print('\nRegister straight spinal cord to template...')
nb_iterations = '50x15'
# TODO: nb iteration for step 2
sct.run('sct_register_multimodal.py -i data_rpi_straight2templateAffine.nii -d '+fname_template+' -s segmentation_rpi_straight2templateAffine.nii.gz -t '+fname_template_seg+' -r '+str(remove_temp_files)+' -n '+nb_iterations+' -v '+str(verbose)+' -x 1',verbose)
# status, output = sct.run('sct_register_straight_spinalcord_to_template.py -i data_rpi_straight.nii.gz -l landmarks_rpi_cross3x3_straight.nii.gz -t '+path_template+'/MNI-Poly-AMU_T2.nii.gz -f template_label_cross.nii.gz -m '+path_template+'/mask_gaussian_templatespace_sigma20.nii.gz -r 1 -n '+nb_iterations+' -v 1')
# Concatenate warping fields: template2anat & anat2template
print('\nConcatenate warping fields: template2anat & anat2template...')
cmd = 'ComposeMultiTransform 3 warp_template2anat.nii.gz -R data.nii warp_straight2curve.nii.gz -i straight2templateAffine.txt warp_dest2src.nii.gz'
print '>> '+cmd
commands.getstatusoutput(cmd)
cmd = 'ComposeMultiTransform 3 warp_anat2template.nii.gz -R '+fname_template+' warp_src2dest.nii.gz straight2templateAffine.txt warp_curve2straight.nii.gz'
print '>> '+cmd
commands.getstatusoutput(cmd)
# Apply warping fields to anat and template
if output_type == 1:
sct.run('WarpImageMultiTransform 3 '+fname_template+' template2anat.nii.gz -R data.nii warp_template2anat.nii.gz')
sct.run('WarpImageMultiTransform 3 data.nii.gz anat2template.nii.gz -R '+fname_template+' warp_anat2template.nii.gz')
# come back to parent folder
os.chdir('..')
# Generate output files
print('\nGenerate output files...')
sct.generate_output_file(path_tmp+'/warp_template2anat.nii.gz','','warp_template2anat','.nii.gz')
sct.generate_output_file(path_tmp+'/warp_anat2template.nii.gz','','warp_anat2template','.nii.gz')
if output_type == 1:
sct.generate_output_file(path_tmp+'/template2anat.nii.gz','','template2anat',ext_data)
sct.generate_output_file(path_tmp+'/anat2template.nii.gz','','anat2template',ext_data)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s'
# to view results
print '\nTo view results, type:'
print 'fslview template2anat '+fname_data+' &'
print 'fslview anat2template '+fname_template+' &\n'
def main():
#Initialization
fname = ''
verbose = param.verbose
output_name = param.output_name
smoothness = param.smoothness
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:o:s:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
fname = arg
elif opt in ("-o"):
output_name = arg
elif opt in ("-s"):
smoothness = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname == '' :
usage()
# check existence of input files
print'\nCheck if file exists ...'
sct.check_file_exist(fname)
# check if RPI
sct.check_if_rpi(fname)
# Display arguments
print'\nCheck input arguments...'
print' Input volume ...................... '+fname
print' Verbose ........................... '+str(verbose)
file = load(fname)
data = file.get_data()
hdr = file.get_header()
X,Y,Z = (data>0).nonzero()
Z_new = linspace(min(Z),max(Z),(max(Z)-min(Z)+1))
# tck1 = interpolate.splrep(Z, X, s=200)
# X_fit = interpolate.splev(Z_new, tck1)
#
# tck2 = interpolate.splrep(Z, Y, s=200)
# Y_fit = interpolate.splev(Z_new, tck2)
# f1 = interpolate.interp1d(Z, X, kind='cubic')
# f2 = interpolate.interp1d(Z,Y, kind='cubic')
#
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
print X, Y, Z
# NURBS!
#X_fit, Y_fit, Z_fit, x_deriv, y_deriv, z_deriv = b_spline_nurbs(X, Y, Z, degree=3, point_number=3000)
#f_opt_x, f_opt_y = opt_f(X,Y,Z)
#print "f_opt = "+str(f_opt_x)+" "+str(f_opt_y)
#f1 = non_parametric(Z,X,f=0.8)
#f2 = non_parametric(Z,Y,f=0.8)
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
#f1 = polynomial_fit(Z,X,smoothness)
#f2 = polynomial_fit(Z,Y,smoothness)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
print X_fit
print Y_fit
if verbose==2 :
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new,X_fit)
plt.plot(Z,X,'o',linestyle = 'None')
plt.show()
plt.figure()
plt.plot(Z_new,Y_fit)
plt.plot(Z,Y,'o',linestyle = 'None')
plt.show()
data =data*0
for i in xrange(len(X_fit)):
data[X_fit[i],Y_fit[i],Z_new[i]] = 1
print '\nSave volume ...'
hdr.set_data_dtype('float32') # set imagetype to uint8
# save volume
#data = data.astype(float32, copy =False)
img = Nifti1Image(data, None, hdr)
file_name = output_name
save(img,file_name)
print '\nFile created : ' + output_name
del data
def main():
# Initialization
fname_warp_list = '' # list of warping fields
fname_dest = '' # destination image (fix)
fname_warp_final = '' # concatenated transformations
verbose = 1
# Parameters for debug mode
if param.debug:
sct.printv('\n*** WARNING: DEBUG MODE ON ***\n')
status, path_sct_data = getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_warp_list = path_sct_data + '/t2/warp_template2anat.nii.gz,-' + path_sct_data + '/mt/warp_template2mt.nii.gz'
fname_dest = path_sct_data + '/mt/mtr.nii.gz'
verbose = 1
else:
# Check input parameters
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_dest = arguments['-d']
fname_warp_list = arguments['-w']
if '-o' in arguments:
fname_warp_final = arguments['-o']
verbose = int(arguments['-v'])
# Parse list of warping fields
sct.printv('\nParse list of transformations...', verbose)
use_inverse = []
fname_warp_list_invert = []
for i in range(len(fname_warp_list)):
# Check if inverse matrix is specified with '-' at the beginning of file name
if fname_warp_list[i].find('-') == 0:
use_inverse.append('-i ')
fname_warp_list[i] = fname_warp_list[i][1:] # remove '-'
else:
use_inverse.append('')
sct.printv(
' Transfo #' + str(i) + ': ' + use_inverse[i] +
fname_warp_list[i], verbose)
fname_warp_list_invert.append(use_inverse[i] + fname_warp_list[i])
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_dest, verbose)
for i in range(len(fname_warp_list)):
sct.check_file_exist(fname_warp_list[i], verbose)
# Get output folder and file name
if fname_warp_final == '':
path_out, file_out, ext_out = sct.extract_fname(param.fname_warp_final)
else:
path_out, file_out, ext_out = sct.extract_fname(fname_warp_final)
# Check dimension of destination data (cf. issue #1419, #1429)
im_dest = Image(fname_dest)
if im_dest.dim[2] == 1:
dimensionality = '2'
else:
dimensionality = '3'
# Concatenate warping fields
sct.printv('\nConcatenate warping fields...', verbose)
# N.B. Here we take the inverse of the warp list
fname_warp_list_invert.reverse()
cmd = 'isct_ComposeMultiTransform ' + dimensionality + ' warp_final' + ext_out + ' -R ' + fname_dest + ' ' + ' '.join(
fname_warp_list_invert)
sct.printv('>> ' + cmd, verbose)
status, output = getstatusoutput(
cmd
) # here cannot use sct.run() because of wrong output status in isct_ComposeMultiTransform
# check if output was generated
if not os.path.isfile('warp_final' + ext_out):
sct.printv('ERROR: Warping field was not generated.\n' + output, 1,
'error')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file('warp_final' + ext_out,
path_out + file_out + ext_out)
def main():
# Initialization
fname_data = ''
suffix_out = '_crop'
remove_temp_files = param.remove_temp_files
verbose = param.verbose
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
remove_temp_files = param.remove_temp_files
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = path_sct + '/testing/data/errsm_23/t2/t2.nii.gz'
remove_temp_files = 0
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:r:v:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) +
': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# print arguments
print '\nCheck parameters:'
print ' data ................... ' + fname_data
print
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S") + '/'
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.run('isct_c3d ' + fname_data + ' -o ' + path_tmp + 'data.nii')
# go to tmp folder
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
set_orientation('data.nii', 'RPI', 'data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title(
'Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.'
)
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n',
1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.setFileName('data_rpi_crop.nii')
nii.save()
# come back to parent folder
os.chdir('..')
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + 'data_rpi_crop.nii',
path_out + file_out + ext_out)
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
# to view results
print '\nDone! To view results, type:'
print 'fslview ' + path_out + file_out + ext_out + ' &'
print
def main():
# Initialization
fname_src = '' # source image (moving)
fname_warp_list = '' # list of warping fields
fname_dest = '' # destination image (fix)
fname_src_reg = ''
verbose = 1
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
crop_reference = 0 # if = 1, put 0 everywhere around warping field, if = 2, real crop
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
# get path of the testing data
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_src = path_sct_data+'/template/MNI-Poly-AMU_T2.nii.gz'
fname_warp_list = path_sct_data+'/t2/warp_template2anat.nii.gz'
fname_dest = path_sct_data+'/t2/t2.nii.gz'
verbose = 1
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:d:o:v:w:x:c:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_src = arg
elif opt in ('-d'):
fname_dest = arg
elif opt in ('-o'):
fname_src_reg = arg
elif opt in ('-x'):
param.interp = arg
elif opt in ('-v'):
verbose = int(arg)
elif opt in ('-w'):
fname_warp_list = arg
elif opt in ('-c'):
crop_reference = int(arg)
# display usage if a mandatory argument is not provided
if fname_src == '' or fname_warp_list == '' or fname_dest == '':
usage()
# get the right interpolation field depending on method
interp = sct.get_interpolation('isct_antsApplyTransforms', param.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
fname_warp_list = fname_warp_list.replace(' ', '') # remove spaces
fname_warp_list = fname_warp_list.split(",") # parse with comma
for i in range(len(fname_warp_list)):
# Check if inverse matrix is specified with '-' at the beginning of file name
if fname_warp_list[i].find('-') == 0:
use_inverse.append('-i ')
fname_warp_list[i] = fname_warp_list[i][1:] # remove '-'
else:
use_inverse.append('')
sct.printv(' Transfo #'+str(i)+': '+use_inverse[i]+fname_warp_list[i], verbose)
fname_warp_list_invert.append(use_inverse[i]+fname_warp_list[i])
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1])
if ext_fname in ['.txt','.mat']:
isLastAffine = True
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_src)
sct.check_file_exist(fname_dest)
for i in range(len(fname_warp_list)):
# check if file exist
sct.check_file_exist(fname_warp_list[i])
for i in range(len(fname_warp_list_invert)):
sct.check_file_exist(fname_warp_list_invert[i])
# check if destination file is 3d
sct.check_if_3d(fname_dest)
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
# Extract path, file and extension
# path_src, file_src, ext_src = sct.extract_fname(os.path.abspath(fname_src))
# fname_dest = os.path.abspath(fname_dest)
path_src, file_src, ext_src = sct.extract_fname(fname_src)
# fname_dest = os.path.abspath(fname_dest
# Get output folder and file name
if fname_src_reg == '':
path_out = '' # output in user's current directory
file_out = file_src+'_reg'
ext_out = ext_src
fname_out = path_out+file_out+ext_out
else:
# path_out, file_out, ext_out = sct.extract_fname(fname_src_reg)
fname_out = fname_src_reg
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('isct_antsApplyTransforms -d 3 -i '+fname_src+' -o '+fname_out+' -t '+' '.join(fname_warp_list_invert)+' -r '+fname_dest+interp, verbose)
# if 4d, loop across the T dimension
else:
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder
# NB: cannot use c3d here because c3d cannot convert 4D data.
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('cp '+fname_src+' '+path_tmp+'data'+ext_src, verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
sct.run('fslchfiletype NIFTI data', verbose)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
sct.run(fsloutput+'fslsplit data data_T', verbose)
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T'+str(it).zfill(4)+'.nii'
file_data_split_reg = 'data_reg_T'+str(it).zfill(4)+'.nii'
sct.run('isct_antsApplyTransforms -d 3 -i '+file_data_split+' -o '+file_data_split_reg+' -t '+' '.join(fname_warp_list_invert)+' -r '+fname_dest+interp, verbose)
# Merge files back
sct.printv('\nMerge file back...', verbose)
cmd = fsloutput+'fslmerge -t '+fname_out
for it in range(nt):
file_data_split_reg = 'data_reg_T'+str(it).zfill(4)+'.nii'
cmd = cmd+' '+file_data_split_reg
sct.run(cmd, param.verbose)
# come back to parent folder
os.chdir('..')
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv('WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...',1,'warning')
elif crop_reference == 1:
sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
# display elapsed time
sct.printv('\nDone! To view results, type:', verbose)
sct.printv('fslview '+fname_dest+' '+fname_out+' &\n', verbose, 'info')
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix + file_data + ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. ' + orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp + 'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp + 'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp + 'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0',
verbose=False)
if method_type == 'centerline':
sct.run(
'sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
if method_type == 'point':
sct.run(
'sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv(
'WARNING in ' + os.path.basename(__file__) +
': Input image is 4d but output mask will 3D.', param.verbose,
'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(
'sct_label_utils -i point_RPI.nii.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx) / 2), round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center,
param.shape,
param.size,
nx,
ny,
even=param.even,
spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run(
'sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' +
orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp + 'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf ' + path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv(
'fslview ' + param.fname_data + ' ' + param.fname_out +
' -l Red -t 0.5 &', param.verbose, 'info')
print
def main(args=None):
"""
Main function
:param args:
:return:
"""
# get parser args
if args is None:
args = None if sys.argv[1:] else ['--help']
else:
# flatten the list of input arguments because -w and -winv carry a nested list
lst = []
for line in args:
lst.append(line) if isinstance(line, str) else lst.extend(line)
args = lst
parser = get_parser()
arguments = parser.parse_args(args=args)
# Initialization
fname_warp_final = '' # concatenated transformations
fname_dest = arguments.d
fname_warp_list = arguments.w
warpinv_filename = arguments.winv
if arguments.o is not None:
fname_warp_final = arguments.o
verbose = arguments.v
sct.init_sct(log_level=verbose, update=True) # Update log level
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# list_warp = list_warp.replace(' ', '') # remove spaces
# list_warp = list_warp.split(",") # parse with comma
for idx_warp, path_warp in enumerate(fname_warp_list):
# Check if this transformation should be inverted
if path_warp in warpinv_filename:
use_inverse.append('-i')
# list_warp[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[
use_inverse[idx_warp], fname_warp_list[idx_warp]
]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = fname_warp_list[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and Image(fname_warp_list[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(
fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x, y: x + y,
fname_warp_list_invert)
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_dest, verbose)
for i in range(len(fname_warp_list)):
sct.check_file_exist(fname_warp_list[i], verbose)
# Get output folder and file name
if fname_warp_final == '':
path_out, file_out, ext_out = sct.extract_fname(param.fname_warp_final)
else:
path_out, file_out, ext_out = sct.extract_fname(fname_warp_final)
# Check dimension of destination data (cf. issue #1419, #1429)
im_dest = Image(fname_dest)
if im_dest.dim[2] == 1:
dimensionality = '2'
else:
dimensionality = '3'
cmd = [
'isct_ComposeMultiTransform', dimensionality, 'warp_final' + ext_out,
'-R', fname_dest
] + fname_warp_list_invert
status, output = sct.run(cmd, verbose=verbose, is_sct_binary=True)
# check if output was generated
if not os.path.isfile('warp_final' + ext_out):
sct.printv('ERROR: Warping field was not generated.\n' + output, 1,
'error')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file('warp_final' + ext_out,
os.path.join(path_out, file_out + ext_out))
