def create_line(param, fname, coord, nz):
"""
Create vertical line in 3D volume
:param param:
:param fname:
:param coord:
:param nz:
:return:
"""
# duplicate volume (assumes input file is nifti)
sct.copy(fname, 'line.nii', verbose=param.verbose)
# set all voxels to zero
run_proc(['sct_maths', '-i', 'line.nii', '-mul', '0', '-o', 'line.nii'],
param.verbose)
labels = []
if isinstance(coord[0], Coordinate):
for x, y, _, _ in coord:
labels.extend([Coordinate([x, y, iz, 1]) for iz in range(nz)])
else:
# backwards compat
labels.extend(
[Coordinate([coord[0], coord[1], iz, 1]) for iz in range(nz)])
create_labels(Image("line.nii"), labels).save(path="line.nii")
return 'line.nii'
def extract_sagital_slice(self):
"""Extract the sagital slice where the detection is done.
If the segmentation is provided,
the 2D sagital slice is choosen accoding to the segmentation.
If the segmentation is not provided,
the 2D sagital slice is choosen as the mid-sagital slice of the input image.
"""
# TODO: get the mean across multiple sagittal slices to reduce noise
if self.fname_seg is not None:
img_seg = Image(self.fname_seg)
z_mid_slice = img_seg.data[:, int(img_seg.dim[1] / 2), :]
if 1 in z_mid_slice: # if SC segmentation available at this slice
self.rl_coord = int(
center_of_mass(z_mid_slice)[1]) # Right_left coordinate
else:
self.rl_coord = int(img_seg.dim[2] / 2)
del img_seg
else:
img = Image(self.fname_im)
self.rl_coord = int(img.dim[2] / 2) # Right_left coordinate
del img
run_proc([
'sct_crop_image', '-i', self.fname_im, '-zmin',
str(self.rl_coord), '-zmax',
str(self.rl_coord + 1), '-o', self.slice2D_im
])
def checkRAM(os, verbose=1):
if (os == 'linux'):
status, output = utils.run_proc('grep MemTotal /proc/meminfo', 0)
printv('RAM: ' + output)
ram_split = output.split()
ram_total = float(ram_split[1])
status, output = utils.run_proc('free -m', 0)
printv(output)
return ram_total / 1024
elif (os == 'osx'):
status, output = utils.run_proc('hostinfo | grep memory', 0)
printv('RAM: ' + output)
ram_split = output.split(' ')
ram_total = float(ram_split[3])
# Get process info
ps = subprocess.Popen(['ps', '-caxm', '-orss,comm'], stdout=subprocess.PIPE).communicate()[0].decode(sys.stdout.encoding)
vm = subprocess.Popen(['vm_stat'], stdout=subprocess.PIPE).communicate()[0].decode(sys.stdout.encoding)
# Iterate processes
processLines = ps.split('\n')
sep = re.compile(r'[\s]+')
rssTotal = 0 # kB
for row in range(1, len(processLines)):
rowText = processLines[row].strip()
rowElements = sep.split(rowText)
try:
rss = float(rowElements[0]) * 1024
except:
rss = 0 # ignore...
rssTotal += rss
# Process vm_stat
vmLines = vm.split('\n')
sep = re.compile(r':[\s]+')
vmStats = {}
for row in range(1, len(vmLines) - 2):
rowText = vmLines[row].strip()
rowElements = sep.split(rowText)
vmStats[(rowElements[0])] = int(rowElements[1].strip(r'\.')) * 4096
if verbose:
printv(' Wired Memory:\t\t%d MB' % (vmStats["Pages wired down"] / 1024 / 1024))
printv(' Active Memory:\t%d MB' % (vmStats["Pages active"] / 1024 / 1024))
printv(' Inactive Memory:\t%d MB' % (vmStats["Pages inactive"] / 1024 / 1024))
printv(' Free Memory:\t\t%d MB' % (vmStats["Pages free"] / 1024 / 1024))
# printv('Real Mem Total (ps):\t%.3f MB' % ( rssTotal/1024/1024 ))
return ram_total
def init(param_test):
"""
Initialize class: param_test
"""
# initialization
default_args = ['-i t2/t2.nii.gz -c t2 -qc testing-qc'
] # default parameters
param_test.fname_seg = 't2_seg.nii.gz' # output segmentation
param_test.fname_gt = 't2/t2_seg-manual.nii.gz'
# note: propseg does *not* produce the same results across platforms, hence the 0.9 Dice threahold.
# For more details, see: https://github.com/neuropoly/spinalcordtoolbox/issues/2769
param_test.dice_threshold = 0.9
# check if isct_propseg compatibility
# TODO: MAKE SURE THIS CASE WORKS AFTER MAJOR REFACTORING
status_isct_propseg, output_isct_propseg = run_proc('isct_propseg',
verbose=0,
raise_exception=False)
isct_propseg_version = output_isct_propseg.split('\n')[0]
if isct_propseg_version != 'sct_propseg - Version 1.1 (2015-03-24)':
status = 99
param_test.output += '\nERROR: isct_propseg does not seem to be compatible with your system or is no up-to-date... Please contact SCT administrators.'
return param_test
# assign default params
if not param_test.args:
param_test.args = default_args
return param_test
def detect(self):
"""Run the classifier on self.slice2D_im."""
sct.printv('\nRun PMJ detector', self.verbose, 'normal')
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_pmj = [
'isct_spine_detect', self.pmj_model,
self.slice2D_im.split('.nii')[0], self.dection_map_pmj
]
print(cmd_pmj)
run_proc(cmd_pmj, verbose=0, is_sct_binary=True)
img = nib.load(self.dection_map_pmj +
'_svm.hdr') # convert .img and .hdr files to .nii
nib.save(img, self.dection_map_pmj + '.nii')
self.dection_map_pmj += '.nii' # fname of the resulting detection map
def test(data_path):
# parameters
folder_data = ['t2/', 'dmri/']
file_data = ['t2.nii.gz', 'dmri.nii.gz']
# test 3d data
cmd = 'sct_orientation -i ' + data_path + folder_data[0] + file_data[0]
status, output = run_proc(cmd)
# test 4d data
if status == 0:
cmd = 'sct_orientation -i ' + data_path + folder_data[1] + file_data[1]
status, output = run_proc(cmd)
# return
return status, output
def test_isct_propseg_compatibility():
# TODO: Move this check to `sct_check_dependencies`. (It was in `sct_testing`, so it is put here for now.)
status_isct_propseg, output_isct_propseg = run_proc('isct_propseg', verbose=0, raise_exception=False,
is_sct_binary=True)
isct_propseg_version = output_isct_propseg.split('\n')[0]
assert isct_propseg_version == 'sct_propseg - Version 1.1 (2015-03-24)', \
'isct_propseg does not seem to be compatible with your system or is no up-to-date... Please contact SCT ' \
'administrators.'
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = im_in.orientation
if orientation != 'RPI':
fname = im_in.change_orientation(im_in, 'RPI', generate_path=True).save().absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1:
# when data is 2d: we convert it to a 3d image in order to avoid conversion problem with 2d data
# TODO: check if this above problem is still present (now that we are using nibabel instead of nipy)
run_proc(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
run_proc(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
run_proc(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.save(name_resample)
if binary:
run_proc(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample])
if orientation != 'RPI':
name_resample = Image(name_resample) \
.change_orientation(orientation, generate_path=True) \
.save() \
.absolutepath
return name_resample
else:
if orientation != 'RPI':
fname = sct.add_suffix(fname, "_RPI")
im_in = msct_image.change_orientation(im_in, orientation).save(fname)
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
status, out = run_proc(['fslhd', fname_warp])
curdir = os.getcwd()
os.chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.absolutepath = fname_grid
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, '_resample')
run_proc(['sct_resample', '-i', fname_grid, '-f', '3x3x1', '-x', 'nn', '-o', fname_grid_resample])
fname_grid = os.path.join(tmp_dir, fname_grid_resample)
os.chdir(curdir)
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = os.path.join(path_warp, sct.extract_fname(fname_grid)[1] + '_' + file_warp + ext_warp)
run_proc(['sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w', fname_warp, '-o', grid_warped])
if rm_tmp:
sct.rmtree(tmp_dir)
def warp_label(path_label, folder_label, file_label, fname_src, fname_transfo, path_out):
"""
Warp label files according to info_label.txt file
:param path_label:
:param folder_label:
:param file_label:
:param fname_src:
:param fname_transfo:
:param path_out:
:return:
"""
try:
# Read label file
template_label_ids, template_label_names, template_label_file, combined_labels_ids, combined_labels_names, \
combined_labels_id_groups, clusters_apriori = \
spinalcordtoolbox.metadata.read_label_file(os.path.join(path_label, folder_label), file_label)
except Exception as error:
sct.printv('\nWARNING: Cannot warp label ' + folder_label + ': ' + str(error), 1, 'warning')
raise
else:
# create output folder
if not os.path.exists(os.path.join(path_out, folder_label)):
os.makedirs(os.path.join(path_out, folder_label))
# Warp label
for i in range(0, len(template_label_file)):
fname_label = os.path.join(path_label, folder_label, template_label_file[i])
# apply transfo
run_proc('isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(fname_label,
fname_src,
fname_transfo,
os.path.join(path_out, folder_label, template_label_file[i]),
get_interp(template_label_file[i])),
is_sct_binary=True,
verbose=param.verbose)
# Copy list.txt
sct.copy(os.path.join(path_label, folder_label, param.file_info_label), os.path.join(path_out, folder_label))
def init(param_test):
"""
Initialize class: param_test
"""
# Reorient image to sagittal for testing another orientation (and crop to save time)
run_proc(
'sct_image -i dmri/dmri.nii.gz -setorient AIL -o dmri/dmri_AIL.nii',
verbose=0)
run_proc(
'sct_crop_image -i dmri/dmri_AIL.nii -zmin 19 -zmax 21 -o dmri/dmri_AIL_crop.nii',
verbose=0)
# Create Gaussian mask for testing
run_proc(
'sct_create_mask -i dmri/dmri_T0000.nii.gz -p center -size 5mm -f gaussian -o dmri/mask.nii',
verbose=0)
# initialization
default_args = [
'-i dmri/dmri.nii.gz -bvec dmri/bvecs.txt -g 3 -x nn -ofolder dmri_test1 -r 0',
'-i dmri/dmri.nii.gz -bvec dmri/bvecs.txt -g 3 -m dmri/mask.nii -ofolder dmri_test2 -r 0',
'-i dmri/dmri_AIL_crop.nii -bvec dmri/bvecs.txt -x nn -ofolder dmri_test3 -r 0',
]
# Output moco param files
param_test.file_mocoparam = [
'dmri_test1/moco_params.tsv',
'dmri_test2/moco_params.tsv',
None,
]
# Ground truth value for integrity testing (corresponds to X motion parameters column)
param_test.groundtruth = [
[
0.00047529041677414337, -1.1970542445283172e-05,
-1.1970542445283172e-05, -1.1970542445283172e-05,
-0.1296642741802682, -0.1296642741802682, -0.1296642741802682
],
[
0.008491259664160821, 0.0029020670607778237, 0.0029020670607778237,
0.0029020670607778237, -0.014405996135095476,
-0.014405996135095476, -0.014405996135095476
],
None,
]
# assign default params
if not param_test.args:
param_test.args = default_args
return param_test
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()
# Parameters for debug mode
if param.debug:
fname_data = os.path.join(sct.__data_dir__, 'sct_testing_data', 't2',
't2_seg.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)
run_proc([
"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)
run_proc([
"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)
run_proc([
"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(np.round(elapsed_time))) + 's')
# to view results
sct.printv('\nTo view results, type:')
sct.printv('fslview ' + file_data + ' ' + file_data + suffix + ' &\n')
def detect_c2c3(nii_im, nii_seg, contrast, nb_sag_avg=7.0, verbose=1):
"""
Detect the posterior edge of C2-C3 disc.
:param nii_im:
:param nii_seg:
:param contrast:
:param verbose:
:return:
"""
# path to the pmj detector
path_model = os.path.join(sct.__data_dir__, 'c2c3_disc_models',
'{}_model'.format(contrast))
# check if model exists
if not os.path.isfile(path_model + '.yml'):
raise FileNotFoundError(
"The model file {} does not exist. Please download it using sct_download_data"
.format(path_model + '.yml'))
orientation_init = nii_im.orientation
z_seg_max = np.max(np.where(nii_seg.change_orientation('PIR').data)[1])
# Flatten sagittal
nii_im = flatten_sagittal(nii_im, nii_seg, verbose=verbose)
nii_seg_flat = flatten_sagittal(nii_seg, nii_seg, verbose=verbose)
# create temporary folder with intermediate results
logger.info("Creating temporary folder...")
tmp_folder = sct.TempFolder()
tmp_folder.chdir()
# Extract mid-slice
nii_im.change_orientation('PIR')
nii_seg_flat.change_orientation('PIR')
mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2))
nb_sag_avg_half = int(nb_sag_avg / 2 / nii_im.dim[6])
midSlice = np.mean(nii_im.data[:, :, mid_RL - nb_sag_avg_half:mid_RL +
nb_sag_avg_half + 1], 2) # average 7 slices
midSlice_seg = nii_seg_flat.data[:, :, mid_RL]
nii_midSlice = zeros_like(nii_im)
nii_midSlice.data = midSlice
nii_midSlice.save('data_midSlice.nii')
# Run detection
logger.info('Run C2-C3 detector...')
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \
(path_model, 'data_midSlice', 'data_midSlice_pred')
# The command below will fail, but we don't care because it will output an image (prediction), which we
# will use later on.
s, o = run_proc(cmd_detection,
verbose=0,
is_sct_binary=True,
raise_exception=False)
pred = nib.load('data_midSlice_pred_svm.hdr').get_data()
if verbose >= 2:
# copy the "prediction data before post-processing" in an Image object
nii_pred_before_postPro = nii_midSlice.copy()
nii_pred_before_postPro.data = pred # 2D data with orientation, mid sag slice of the original data
nii_pred_before_postPro.save(
"pred_midSlice_before_postPro.nii.gz") # save it)
# DEBUG trick: check if the detection succeed by running: fsleyes data_midSlice data_midSlice_pred_svm -cm red -dr 0 100
# If a "red cluster" is observed in the neighbourhood of C2C3, then the model detected it.
# Create mask along centerline
midSlice_mask = np.zeros(midSlice_seg.shape)
mask_halfSize = int(np.rint(25.0 / nii_midSlice.dim[4]))
for z in range(midSlice_mask.shape[1]):
row = midSlice_seg[:,
z] # 2D data with PI orientation, mid sag slice of the original data
if np.any(row > 0):
med_y = int(np.rint(np.median(np.where(row > 0))))
midSlice_mask[
med_y - mask_halfSize:med_y + mask_halfSize,
z] = 1 # 2D data with PI orientation, mid sag slice of the original data
if verbose >= 2:
# copy the created mask in an Image object
nii_postPro_mask = nii_midSlice.copy()
nii_postPro_mask.data = midSlice_mask # 2D data with PI orientation, mid sag slice of the original data
nii_postPro_mask.save("mask_midSlice.nii.gz") # save it
# mask prediction
pred[midSlice_mask == 0] = 0
pred[:, z_seg_max:] = 0 # Mask above SC segmentation
if verbose >= 2:
# copy the "prediction data after post-processing" in an Image object
nii_pred_after_postPro = nii_midSlice.copy()
nii_pred_after_postPro.data = pred
nii_pred_after_postPro.save(
"pred_midSlice_after_postPro.nii.gz") # save it
# assign label to voxel
nii_c2c3 = zeros_like(nii_seg_flat) # 3D data with PIR orientaion
if np.any(pred > 0):
logger.info('C2-C3 detected...')
pred_bin = (pred > 0).astype(np.int_)
coord_max = np.where(pred == np.max(pred))
pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0]
nii_seg.change_orientation('PIR')
rl_c2c3 = int(
np.rint(center_of_mass(np.array(nii_seg.data[:, is_c2c3, :]))[1]))
nii_c2c3.data[pa_c2c3, is_c2c3, rl_c2c3] = 3
else:
logger.warning('C2-C3 not detected...')
# remove temporary files
tmp_folder.chdir_undo()
if verbose < 2:
logger.info("Remove temporary files...")
tmp_folder.cleanup()
else:
logger.info("Temporary files saved to " + tmp_folder.get_path())
nii_c2c3.change_orientation(orientation_init)
return nii_c2c3
def main():
# Initialization
size_data = 61
size_label = 1 # put zero for labels that are single points.
dice_acceptable = 0.39 # computed DICE should be 0.931034
test_passed = 0
remove_temp_files = 1
verbose = 1
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hvr:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-v'):
verbose = int(arg)
elif opt in ('-r'):
remove_temp_files = int(arg)
path_tmp = sct.tmp_create(basename="test_ants", verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Initialise numpy volumes
data_src = np.zeros((size_data, size_data, size_data), dtype=np.int16)
data_dest = np.zeros((size_data, size_data, size_data), dtype=np.int16)
# add labels for src image (curved).
# Labels can be big (more than single point), because when applying NN interpolation, single points might disappear
data_src[20 - size_label:20 + size_label + 1, 20 - size_label:20 + size_label + 1, 10 - size_label:10 + size_label + 1] = 1
data_src[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1] = 2
data_src[20 - size_label:20 + size_label + 1, 20 - size_label:20 + size_label + 1, 50 - size_label:50 + size_label + 1] = 3
# add labels for dest image (straight).
# Here, no need for big labels (bigger than single point) because these labels will not be re-interpolated.
data_dest[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 10 - size_label:10 + size_label + 1] = 1
data_dest[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1] = 2
data_dest[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 50 - size_label:50 + size_label + 1] = 3
# save as nifti
img_src = nib.Nifti1Image(data_src, np.eye(4))
nib.save(img_src, 'data_src.nii.gz')
img_dest = nib.Nifti1Image(data_dest, np.eye(4))
nib.save(img_dest, 'data_dest.nii.gz')
# Estimate rigid transformation
sct.printv('\nEstimate rigid transformation between paired landmarks...', verbose)
# TODO fixup isct_ants* parsers
run_proc(['isct_antsRegistration',
'-d', '3',
'-t', 'syn[1,3,1]',
'-m', 'MeanSquares[data_dest.nii.gz,data_src.nii.gz,1,3]',
'-f', '2',
'-s', '0',
'-o', '[src2reg,data_src_reg.nii.gz]',
'-c', '5',
'-v', '1',
'-n', 'NearestNeighbor'], verbose, is_sct_binary=True)
# # Apply rigid transformation
# sct.printv('\nApply rigid transformation to curved landmarks...', verbose)
# run_proc('sct_apply_transfo -i data_src.nii.gz -o data_src_rigid.nii.gz -d data_dest.nii.gz -w curve2straight_rigid.txt -p nn', verbose)
#
# # Estimate b-spline transformation curve --> straight
# sct.printv('\nEstimate b-spline transformation: curve --> straight...', verbose)
# run_proc('isct_ANTSLandmarksBSplineTransform data_dest.nii.gz data_src_rigid.nii.gz warp_curve2straight_intermediate.nii.gz 5x5x5 3 2 0', verbose)
#
# # Concatenate rigid and non-linear transformations...
# sct.printv('\nConcatenate rigid and non-linear transformations...', verbose)
# cmd = 'isct_ComposeMultiTransform 3 warp_curve2straight.nii.gz -R data_dest.nii.gz warp_curve2straight_intermediate.nii.gz curve2straight_rigid.txt'
# sct.printv('>> '+cmd, verbose)
# run_proc(cmd)
#
# # Apply deformation to input image
# sct.printv('\nApply transformation to input image...', verbose)
# run_proc('sct_apply_transfo -i data_src.nii.gz -o data_src_warp.nii.gz -d data_dest.nii.gz -w warp_curve2straight.nii.gz -p nn', verbose)
#
# Compute DICE coefficient between src and dest
sct.printv('\nCompute DICE coefficient...', verbose)
run_proc(["sct_dice_coefficient",
"-i", "data_dest.nii.gz",
"-d", "data_src_reg.nii.gz",
"-o", "dice.txt"], verbose)
with open("dice.txt", "r") as file_dice:
dice = float(file_dice.read().replace('3D Dice coefficient = ', ''))
sct.printv('Dice coeff = ' + str(dice) + ' (should be above ' + str(dice_acceptable) + ')', verbose)
# Check if DICE coefficient is above acceptable value
if dice > dice_acceptable:
test_passed = 1
# come back
os.chdir(curdir)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp)
# output result for parent function
if test_passed:
sct.printv('\nTest passed!\n', verbose)
sys.exit(0)
else:
sct.printv('\nTest failed!\n', verbose)
sys.exit(1)
tmp_dir = os.path.abspath(tmp_dir)
# copy input files to tmp directory
# for fname in [fname_input1, fname_input2]:
sct.copy(fname_input1, tmp_dir)
sct.copy(fname_input2, tmp_dir)
fname_input1 = ''.join(sct.extract_fname(fname_input1)[1:])
fname_input2 = ''.join(sct.extract_fname(fname_input2)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir) # go to tmp directory
if arguments.bin is not None:
fname_input1_bin = sct.add_suffix(fname_input1, '_bin')
run_proc([
'sct_maths', '-i', fname_input1, '-bin', '0', '-o',
fname_input1_bin
])
fname_input1 = fname_input1_bin
fname_input2_bin = sct.add_suffix(fname_input2, '_bin')
run_proc([
'sct_maths', '-i', fname_input2, '-bin', '0', '-o',
fname_input2_bin
])
fname_input2 = fname_input2_bin
# copy header of im_1 to im_2
from spinalcordtoolbox.image import Image
im_1 = Image(fname_input1)
im_2 = Image(fname_input2)
im_2.header = im_1.header
im_2.save()
def main():
print("SCT info:")
print("- version: {}".format(sct.__version__))
print("- path: {0}".format(sct.__sct_dir__))
# initialization
install_software = 0
e = 0
complete_test = param.complete_test
os_running = 'not identified'
# Check input parameters
parser = get_parser()
arguments = parser.parse_args()
if arguments.complete:
complete_test = 1
# use variable "verbose" when calling sct.run for more clarity
verbose = complete_test
# complete test
if complete_test:
print(run_proc('date', verbose))
print(run_proc('whoami', verbose))
print(run_proc('pwd', verbose))
bash_profile = os.path.expanduser("~/.bash_profile")
if os.path.isfile(bash_profile):
with io.open(bash_profile, "r") as f:
print(f.read())
bashrc = os.path.expanduser("~/.bashrc")
if os.path.isfile(bashrc):
with io.open(bashrc, "r") as f:
print(f.read())
# check OS
platform_running = sys.platform
if platform_running.find('darwin') != -1:
os_running = 'osx'
elif platform_running.find('linux') != -1:
os_running = 'linux'
print('OS: ' + os_running + ' (' + platform.platform() + ')')
# Check number of CPU cores
from multiprocessing import cpu_count
output = int(os.getenv('ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS', 0))
print('CPU cores: Available: {}, Used by SCT: {}'.format(
cpu_count(), output))
# check RAM
sct.checkRAM(os_running, 0)
if arguments.short:
sys.exit()
# check if Python path is within SCT path
print_line('Check Python executable')
path_python = sys.executable
if sct.__sct_dir__ in path_python:
print_ok()
print(' Using bundled python {} at {}'.format(sys.version,
path_python))
else:
print_warning()
print(' Using system python which is unsupported: {}'.format(
path_python))
# check if data folder is empty
print_line('Check if data are installed')
if os.path.isdir(sct.__data_dir__):
print_ok()
else:
print_fail()
for dep_pkg, dep_ver_spec in get_dependencies():
if dep_ver_spec is None:
print_line('Check if %s is installed' % (dep_pkg))
else:
print_line('Check if %s (%s) is installed' %
(dep_pkg, dep_ver_spec))
try:
module_name, suppress_stderr = resolve_module(dep_pkg)
module = module_import(module_name, suppress_stderr)
version = get_version(module)
if dep_ver_spec is not None and version is not None and dep_ver_spec != version:
print_warning(more=(" (%s != %s mandated version))" %
(version, dep_ver_spec)))
elif version is not None:
print_ok(more=(" (%s)" % version))
else:
print_ok()
except ImportError as err:
print_fail()
print(err)
install_software = 1
print_line('Check if spinalcordtoolbox is installed')
try:
importlib.import_module('spinalcordtoolbox')
print_ok()
except ImportError:
print_fail()
install_software = 1
# Check ANTs integrity
print_line('Check ANTs compatibility with OS ')
cmd = 'isct_test_ants'
status, output = run_proc(cmd, verbose=0, raise_exception=False)
if status == 0:
print_ok()
else:
print_fail()
print(output)
e = 1
if complete_test:
print('>> ' + cmd)
print((status, output), '\n')
# check PropSeg compatibility with OS
print_line('Check PropSeg compatibility with OS ')
status, output = run_proc('isct_propseg',
verbose=0,
raise_exception=False,
is_sct_binary=True)
if status in (0, 1):
print_ok()
else:
print_fail()
print(output)
e = 1
if complete_test:
print((status, output), '\n')
print_line('Check if DISPLAY variable is set')
try:
os.environ['DISPLAY']
print_ok()
# Further check with PyQt specifically
print_line('Check if figure can be opened with PyQt')
from PyQt5.QtWidgets import QApplication, QLabel
try:
app = QApplication([])
label = QLabel('Hello World!')
label.show()
label.close()
print_ok()
except Exception as err:
print_fail()
print(err)
except KeyError:
print_fail()
print('')
sys.exit(e + install_software)
def main(args=None):
# Initialization
param = Param()
start_time = time.time()
parser = get_parser()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_anat = arguments.i
fname_centerline = arguments.s
param.algo_fitting = arguments.algo_fitting
if arguments.smooth is not None:
sigma = arguments.smooth
remove_temp_files = arguments.r
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from spinalcordtoolbox.image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv(
'WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat,
verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# 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)
path_tmp = sct.tmp_create(basename="smooth_spinalcord", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.copy(fname_anat, os.path.join(path_tmp, "anat" + ext_anat))
sct.copy(fname_centerline,
os.path.join(path_tmp, "centerline" + ext_centerline))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = msct_image.Image("anat.nii") \
.change_orientation("RPI", generate_path=True) \
.save() \
.absolutepath
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = msct_image.Image("centerline.nii") \
.change_orientation("RPI", generate_path=True) \
.save() \
.absolutepath
# Straighten the spinal cord
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...',
verbose)
cache_sig = sct.cache_signature(
input_files=[fname_anat_rpi, fname_centerline_rpi],
input_params={"x": "spline"})
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(
os.path.join(
curdir, 'warp_curve2straight.nii.gz')) and os.path.isfile(
os.path.join(
curdir,
'warp_straight2curve.nii.gz')) and os.path.isfile(
os.path.join(curdir, 'straight_ref.nii.gz')):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(os.path.join(curdir, 'warp_curve2straight.nii.gz'),
'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, 'warp_straight2curve.nii.gz'),
'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, 'straight_ref.nii.gz'),
'straight_ref.nii.gz')
# apply straightening
run_proc([
'sct_apply_transfo', '-i', fname_anat_rpi, '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o',
'anat_rpi_straight.nii', '-x', 'spline'
], verbose)
else:
run_proc([
'sct_straighten_spinalcord', '-i', fname_anat_rpi, '-o',
'anat_rpi_straight.nii', '-s', fname_centerline_rpi, '-x',
'spline', '-param', 'algo_fitting=' + param.algo_fitting
], verbose)
sct.cache_save(cachefile, cache_sig)
# move warping fields locally (to use caching next time)
sct.copy('warp_curve2straight.nii.gz',
os.path.join(curdir, 'warp_curve2straight.nii.gz'))
sct.copy('warp_straight2curve.nii.gz',
os.path.join(curdir, 'warp_straight2curve.nii.gz'))
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image...')
sigma_smooth = ",".join([str(i) for i in sigma])
sct_maths.main(args=[
'-i', 'anat_rpi_straight.nii', '-smooth', sigma_smooth, '-o',
'anat_rpi_straight_smooth.nii', '-v', '0'
])
# Apply the reversed warping field to get back the curved spinal cord
sct.printv(
'\nApply the reversed warping field to get back the curved spinal cord...'
)
run_proc([
'sct_apply_transfo', '-i', 'anat_rpi_straight_smooth.nii', '-o',
'anat_rpi_straight_smooth_curved.nii', '-d', 'anat.nii', '-w',
'warp_straight2curve.nii.gz', '-x', 'spline'
], verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.save('anat_rpi_straight_smooth_curved_nonzero.nii')
# come back
os.chdir(curdir)
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(
os.path.join(path_tmp, "anat_rpi_straight_smooth_curved_nonzero.nii"),
file_anat + '_smooth' + ext_anat)
# Remove 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(np.round(elapsed_time))) + 's\n')
sct.display_viewer_syntax([file_anat, file_anat + '_smooth'],
verbose=verbose)
def straighten(self):
"""
Straighten spinal cord. Steps: (everything is done in physical space)
1. open input image and centreline image
2. extract bspline fitting of the centreline, and its derivatives
3. compute length of centerline
4. compute and generate straight space
5. compute transformations
for each voxel of one space: (done using matrices --> improves speed by a factor x300)
a. determine which plane of spinal cord centreline it is included
b. compute the position of the voxel in the plane (X and Y distance from centreline, along the plane)
c. find the correspondant centreline point in the other space
d. find the correspondance of the voxel in the corresponding plane
6. generate warping fields for each transformations
7. write warping fields and apply them
step 5.b: how to find the corresponding plane?
The centerline plane corresponding to a voxel correspond to the nearest point of the centerline.
However, we need to compute the distance between the voxel position and the plane to be sure it is part of the plane and not too distant.
If it is more far than a threshold, warping value should be 0.
step 5.d: how to make the correspondance between centerline point in both images?
Both centerline have the same lenght. Therefore, we can map centerline point via their position along the curve.
If we use the same number of points uniformely along the spinal cord (1000 for example), the correspondance is straight-forward.
:return:
"""
# Initialization
fname_anat = self.input_filename
fname_centerline = self.centerline_filename
fname_output = self.output_filename
remove_temp_files = self.remove_temp_files
verbose = self.verbose
interpolation_warp = self.interpolation_warp # TODO: remove this
# start timer
start_time = time.time()
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_tmp = sct.tmp_create(basename="straighten_spinalcord",
verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopy files to tmp folder...', verbose)
Image(fname_anat).save(os.path.join(path_tmp, "data.nii"))
Image(fname_centerline).save(
os.path.join(path_tmp, "centerline.nii.gz"))
if self.use_straight_reference:
Image(self.centerline_reference_filename).save(
os.path.join(path_tmp, "centerline_ref.nii.gz"))
if self.discs_input_filename != '':
Image(self.discs_input_filename).save(
os.path.join(path_tmp, "labels_input.nii.gz"))
if self.discs_ref_filename != '':
Image(self.discs_ref_filename).save(
os.path.join(path_tmp, "labels_ref.nii.gz"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
image_centerline = Image("centerline.nii.gz").change_orientation(
"RPI").save("centerline_rpi.nii.gz", mutable=True)
# Get dimension
nx, ny, nz, nt, px, py, pz, pt = image_centerline.dim
if self.speed_factor != 1.0:
intermediate_resampling = True
px_r, py_r, pz_r = px * self.speed_factor, py * self.speed_factor, pz * self.speed_factor
else:
intermediate_resampling = False
if intermediate_resampling:
sct.mv('centerline_rpi.nii.gz', 'centerline_rpi_native.nii.gz')
pz_native = pz
# TODO: remove system call
run_proc([
'sct_resample', '-i', 'centerline_rpi_native.nii.gz', '-mm',
str(px_r) + 'x' + str(py_r) + 'x' + str(pz_r), '-o',
'centerline_rpi.nii.gz'
])
image_centerline = Image('centerline_rpi.nii.gz')
nx, ny, nz, nt, px, py, pz, pt = image_centerline.dim
if np.min(image_centerline.data) < 0 or np.max(
image_centerline.data) > 1:
image_centerline.data[image_centerline.data < 0] = 0
image_centerline.data[image_centerline.data > 1] = 1
image_centerline.save()
# 2. extract bspline fitting of the centerline, and its derivatives
img_ctl = Image('centerline_rpi.nii.gz')
centerline = _get_centerline(img_ctl, self.param_centerline, verbose)
number_of_points = centerline.number_of_points
# ==========================================================================================
logger.info('Create the straight space and the safe zone')
# 3. compute length of centerline
# compute the length of the spinal cord based on fitted centerline and size of centerline in z direction
# Computation of the safe zone.
# The safe zone is defined as the length of the spinal cord for which an axial segmentation will be complete
# The safe length (to remove) is computed using the safe radius (given as parameter) and the angle of the
# last centerline point with the inferior-superior direction. Formula: Ls = Rs * sin(angle)
# Calculate Ls for both edges and remove appropriate number of centerline points
radius_safe = 0.0 # mm
# inferior edge
u = centerline.derivatives[0]
v = np.array([0, 0, -1])
angle_inferior = np.arctan2(np.linalg.norm(np.cross(u, v)),
np.dot(u, v))
length_safe_inferior = radius_safe * np.sin(angle_inferior)
# superior edge
u = centerline.derivatives[-1]
v = np.array([0, 0, 1])
angle_superior = np.arctan2(np.linalg.norm(np.cross(u, v)),
np.dot(u, v))
length_safe_superior = radius_safe * np.sin(angle_superior)
# remove points
inferior_bound = bisect.bisect(centerline.progressive_length,
length_safe_inferior) - 1
superior_bound = centerline.number_of_points - bisect.bisect(
centerline.progressive_length_inverse, length_safe_superior)
z_centerline = centerline.points[:, 2]
length_centerline = centerline.length
size_z_centerline = z_centerline[-1] - z_centerline[0]
# compute the size factor between initial centerline and straight bended centerline
factor_curved_straight = length_centerline / size_z_centerline
middle_slice = (z_centerline[0] + z_centerline[-1]) / 2.0
bound_curved = [
z_centerline[inferior_bound], z_centerline[superior_bound]
]
bound_straight = [(z_centerline[inferior_bound] - middle_slice) *
factor_curved_straight + middle_slice,
(z_centerline[superior_bound] - middle_slice) *
factor_curved_straight + middle_slice]
logger.info('Length of spinal cord: {}'.format(length_centerline))
logger.info(
'Size of spinal cord in z direction: {}'.format(size_z_centerline))
logger.info('Ratio length/size: {}'.format(factor_curved_straight))
logger.info(
'Safe zone boundaries (curved space): {}'.format(bound_curved))
logger.info(
'Safe zone boundaries (straight space): {}'.format(bound_straight))
# 4. compute and generate straight space
# points along curved centerline are already regularly spaced.
# calculate position of points along straight centerline
# Create straight NIFTI volumes.
# ==========================================================================================
# TODO: maybe this if case is not needed?
if self.use_straight_reference:
image_centerline_pad = Image('centerline_rpi.nii.gz')
nx, ny, nz, nt, px, py, pz, pt = image_centerline_pad.dim
fname_ref = 'centerline_ref_rpi.nii.gz'
image_centerline_straight = Image('centerline_ref.nii.gz') \
.change_orientation("RPI") \
.save(fname_ref, mutable=True)
centerline_straight = _get_centerline(image_centerline_straight,
self.param_centerline,
verbose)
nx_s, ny_s, nz_s, nt_s, px_s, py_s, pz_s, pt_s = image_centerline_straight.dim
# Prepare warping fields headers
hdr_warp = image_centerline_pad.hdr.copy()
hdr_warp.set_data_dtype('float32')
hdr_warp_s = image_centerline_straight.hdr.copy()
hdr_warp_s.set_data_dtype('float32')
if self.discs_input_filename != "" and self.discs_ref_filename != "":
discs_input_image = Image('labels_input.nii.gz')
coord = discs_input_image.getNonZeroCoordinates(
sorting='z', reverse_coord=True)
coord_physical = []
for c in coord:
c_p = discs_input_image.transfo_pix2phys([[c.x, c.y, c.z]
]).tolist()[0]
c_p.append(c.value)
coord_physical.append(c_p)
centerline.compute_vertebral_distribution(coord_physical)
centerline.save_centerline(
image=discs_input_image,
fname_output='discs_input_image.nii.gz')
discs_ref_image = Image('labels_ref.nii.gz')
coord = discs_ref_image.getNonZeroCoordinates(
sorting='z', reverse_coord=True)
coord_physical = []
for c in coord:
c_p = discs_ref_image.transfo_pix2phys([[c.x, c.y,
c.z]]).tolist()[0]
c_p.append(c.value)
coord_physical.append(c_p)
centerline_straight.compute_vertebral_distribution(
coord_physical)
centerline_straight.save_centerline(
image=discs_ref_image,
fname_output='discs_ref_image.nii.gz')
else:
logger.info(
'Pad input volume to account for spinal cord length...')
start_point, end_point = bound_straight[0], bound_straight[1]
offset_z = 0
# if the destination image is resampled, we still create the straight reference space with the native
# resolution.
# TODO: Maybe this if case is not needed?
if intermediate_resampling:
padding_z = int(
np.ceil(1.5 *
((length_centerline - size_z_centerline) / 2.0) /
pz_native))
run_proc([
'sct_image', '-i', 'centerline_rpi_native.nii.gz', '-o',
'tmp.centerline_pad_native.nii.gz', '-pad',
'0,0,' + str(padding_z)
])
image_centerline_pad = Image('centerline_rpi_native.nii.gz')
nx, ny, nz, nt, px, py, pz, pt = image_centerline_pad.dim
start_point_coord_native = image_centerline_pad.transfo_phys2pix(
[[0, 0, start_point]])[0]
end_point_coord_native = image_centerline_pad.transfo_phys2pix(
[[0, 0, end_point]])[0]
straight_size_x = int(self.xy_size / px)
straight_size_y = int(self.xy_size / py)
warp_space_x = [
int(np.round(nx / 2)) - straight_size_x,
int(np.round(nx / 2)) + straight_size_x
]
warp_space_y = [
int(np.round(ny / 2)) - straight_size_y,
int(np.round(ny / 2)) + straight_size_y
]
if warp_space_x[0] < 0:
warp_space_x[1] += warp_space_x[0] - 2
warp_space_x[0] = 0
if warp_space_y[0] < 0:
warp_space_y[1] += warp_space_y[0] - 2
warp_space_y[0] = 0
spec = dict((
(0, warp_space_x),
(1, warp_space_y),
(2, (0, end_point_coord_native[2] -
start_point_coord_native[2])),
))
msct_image.spatial_crop(
Image("tmp.centerline_pad_native.nii.gz"),
spec).save("tmp.centerline_pad_crop_native.nii.gz")
fname_ref = 'tmp.centerline_pad_crop_native.nii.gz'
offset_z = 4
else:
fname_ref = 'tmp.centerline_pad_crop.nii.gz'
nx, ny, nz, nt, px, py, pz, pt = image_centerline.dim
padding_z = int(
np.ceil(1.5 * ((length_centerline - size_z_centerline) / 2.0) /
pz)) + offset_z
image_centerline_pad = pad_image(image_centerline,
pad_z_i=padding_z,
pad_z_f=padding_z)
nx, ny, nz = image_centerline_pad.data.shape
hdr_warp = image_centerline_pad.hdr.copy()
hdr_warp.set_data_dtype('float32')
start_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, start_point]])[0]
end_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, end_point]])[0]
straight_size_x = int(self.xy_size / px)
straight_size_y = int(self.xy_size / py)
warp_space_x = [
int(np.round(nx / 2)) - straight_size_x,
int(np.round(nx / 2)) + straight_size_x
]
warp_space_y = [
int(np.round(ny / 2)) - straight_size_y,
int(np.round(ny / 2)) + straight_size_y
]
if warp_space_x[0] < 0:
warp_space_x[1] += warp_space_x[0] - 2
warp_space_x[0] = 0
if warp_space_x[1] >= nx:
warp_space_x[1] = nx - 1
if warp_space_y[0] < 0:
warp_space_y[1] += warp_space_y[0] - 2
warp_space_y[0] = 0
if warp_space_y[1] >= ny:
warp_space_y[1] = ny - 1
spec = dict((
(0, warp_space_x),
(1, warp_space_y),
(2, (0, end_point_coord[2] - start_point_coord[2] + offset_z)),
))
image_centerline_straight = msct_image.spatial_crop(
image_centerline_pad, spec)
nx_s, ny_s, nz_s, nt_s, px_s, py_s, pz_s, pt_s = image_centerline_straight.dim
hdr_warp_s = image_centerline_straight.hdr.copy()
hdr_warp_s.set_data_dtype('float32')
if self.template_orientation == 1:
raise NotImplementedError()
start_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, start_point]])[0]
end_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, end_point]])[0]
number_of_voxel = nx * ny * nz
logger.debug('Number of voxels: {}'.format(number_of_voxel))
time_centerlines = time.time()
coord_straight = np.empty((number_of_points, 3))
coord_straight[..., 0] = int(np.round(nx_s / 2))
coord_straight[..., 1] = int(np.round(ny_s / 2))
coord_straight[..., 2] = np.linspace(
0, end_point_coord[2] - start_point_coord[2], number_of_points)
coord_phys_straight = image_centerline_straight.transfo_pix2phys(
coord_straight)
derivs_straight = np.empty((number_of_points, 3))
derivs_straight[..., 0] = derivs_straight[..., 1] = 0
derivs_straight[..., 2] = 1
dx_straight, dy_straight, dz_straight = derivs_straight.T
centerline_straight = Centerline(coord_phys_straight[:, 0],
coord_phys_straight[:, 1],
coord_phys_straight[:, 2],
dx_straight, dy_straight,
dz_straight)
time_centerlines = time.time() - time_centerlines
logger.info('Time to generate centerline: {} ms'.format(
np.round(time_centerlines * 1000.0)))
if verbose == 2:
# TODO: use OO
import matplotlib.pyplot as plt
from datetime import datetime
curved_points = centerline.progressive_length
straight_points = centerline_straight.progressive_length
range_points = np.linspace(0, 1, number_of_points)
dist_curved = np.zeros(number_of_points)
dist_straight = np.zeros(number_of_points)
for i in range(1, number_of_points):
dist_curved[i] = dist_curved[
i - 1] + curved_points[i - 1] / centerline.length
dist_straight[i] = dist_straight[i - 1] + straight_points[
i - 1] / centerline_straight.length
plt.plot(range_points, dist_curved)
plt.plot(range_points, dist_straight)
plt.grid(True)
plt.savefig('fig_straighten_' +
datetime.now().strftime("%y%m%d%H%M%S%f") + '.png')
plt.close()
# alignment_mode = 'length'
alignment_mode = 'levels'
lookup_curved2straight = list(range(centerline.number_of_points))
if self.discs_input_filename != "":
# create look-up table curved to straight
for index in range(centerline.number_of_points):
disc_label = centerline.l_points[index]
if alignment_mode == 'length':
relative_position = centerline.dist_points[index]
else:
relative_position = centerline.dist_points_rel[index]
idx_closest = centerline_straight.get_closest_to_absolute_position(
disc_label,
relative_position,
backup_index=index,
backup_centerline=centerline_straight,
mode=alignment_mode)
if idx_closest is not None:
lookup_curved2straight[index] = idx_closest
else:
lookup_curved2straight[index] = 0
for p in range(0, len(lookup_curved2straight) // 2):
if lookup_curved2straight[p] == lookup_curved2straight[p + 1]:
lookup_curved2straight[p] = 0
else:
break
for p in range(
len(lookup_curved2straight) - 1,
len(lookup_curved2straight) // 2, -1):
if lookup_curved2straight[p] == lookup_curved2straight[p - 1]:
lookup_curved2straight[p] = 0
else:
break
lookup_curved2straight = np.array(lookup_curved2straight)
lookup_straight2curved = list(
range(centerline_straight.number_of_points))
if self.discs_input_filename != "":
for index in range(centerline_straight.number_of_points):
disc_label = centerline_straight.l_points[index]
if alignment_mode == 'length':
relative_position = centerline_straight.dist_points[index]
else:
relative_position = centerline_straight.dist_points_rel[
index]
idx_closest = centerline.get_closest_to_absolute_position(
disc_label,
relative_position,
backup_index=index,
backup_centerline=centerline_straight,
mode=alignment_mode)
if idx_closest is not None:
lookup_straight2curved[index] = idx_closest
for p in range(0, len(lookup_straight2curved) // 2):
if lookup_straight2curved[p] == lookup_straight2curved[p + 1]:
lookup_straight2curved[p] = 0
else:
break
for p in range(
len(lookup_straight2curved) - 1,
len(lookup_straight2curved) // 2, -1):
if lookup_straight2curved[p] == lookup_straight2curved[p - 1]:
lookup_straight2curved[p] = 0
else:
break
lookup_straight2curved = np.array(lookup_straight2curved)
# Create volumes containing curved and straight warping fields
data_warp_curved2straight = np.zeros((nx_s, ny_s, nz_s, 1, 3))
data_warp_straight2curved = np.zeros((nx, ny, nz, 1, 3))
# 5. compute transformations
# Curved and straight images and the same dimensions, so we compute both warping fields at the same time.
# b. determine which plane of spinal cord centreline it is included
# sct.printv(nx * ny * nz, nx_s * ny_s * nz_s)
if self.curved2straight:
for u in sct_progress_bar(range(nz_s)):
x_s, y_s, z_s = np.mgrid[0:nx_s, 0:ny_s, u:u + 1]
indexes_straight = np.array(
list(zip(x_s.ravel(), y_s.ravel(), z_s.ravel())))
physical_coordinates_straight = image_centerline_straight.transfo_pix2phys(
indexes_straight)
nearest_indexes_straight = centerline_straight.find_nearest_indexes(
physical_coordinates_straight)
distances_straight = centerline_straight.get_distances_from_planes(
physical_coordinates_straight, nearest_indexes_straight)
lookup = lookup_straight2curved[nearest_indexes_straight]
indexes_out_distance_straight = np.logical_or(
np.logical_or(
distances_straight > self.threshold_distance,
distances_straight < -self.threshold_distance),
lookup == 0)
projected_points_straight = centerline_straight.get_projected_coordinates_on_planes(
physical_coordinates_straight, nearest_indexes_straight)
coord_in_planes_straight = centerline_straight.get_in_plans_coordinates(
projected_points_straight, nearest_indexes_straight)
coord_straight2curved = centerline.get_inverse_plans_coordinates(
coord_in_planes_straight, lookup)
displacements_straight = coord_straight2curved - physical_coordinates_straight
# Invert Z coordinate as ITK & ANTs physical coordinate system is LPS- (RAI+)
# while ours is LPI-
# Refs: https://sourceforge.net/p/advants/discussion/840261/thread/2a1e9307/#fb5a
# https://www.slicer.org/wiki/Coordinate_systems
displacements_straight[:, 2] = -displacements_straight[:, 2]
displacements_straight[indexes_out_distance_straight] = [
100000.0, 100000.0, 100000.0
]
data_warp_curved2straight[indexes_straight[:, 0], indexes_straight[:, 1], indexes_straight[:, 2], 0, :]\
= -displacements_straight
if self.straight2curved:
for u in sct_progress_bar(range(nz)):
x, y, z = np.mgrid[0:nx, 0:ny, u:u + 1]
indexes = np.array(list(zip(x.ravel(), y.ravel(), z.ravel())))
physical_coordinates = image_centerline_pad.transfo_pix2phys(
indexes)
nearest_indexes_curved = centerline.find_nearest_indexes(
physical_coordinates)
distances_curved = centerline.get_distances_from_planes(
physical_coordinates, nearest_indexes_curved)
lookup = lookup_curved2straight[nearest_indexes_curved]
indexes_out_distance_curved = np.logical_or(
np.logical_or(distances_curved > self.threshold_distance,
distances_curved < -self.threshold_distance),
lookup == 0)
projected_points_curved = centerline.get_projected_coordinates_on_planes(
physical_coordinates, nearest_indexes_curved)
coord_in_planes_curved = centerline.get_in_plans_coordinates(
projected_points_curved, nearest_indexes_curved)
coord_curved2straight = centerline_straight.points[lookup]
coord_curved2straight[:, 0:2] += coord_in_planes_curved[:, 0:2]
coord_curved2straight[:, 2] += distances_curved
displacements_curved = coord_curved2straight - physical_coordinates
displacements_curved[:, 2] = -displacements_curved[:, 2]
displacements_curved[indexes_out_distance_curved] = [
100000.0, 100000.0, 100000.0
]
data_warp_straight2curved[indexes[:, 0], indexes[:, 1],
indexes[:, 2],
0, :] = -displacements_curved
# Creation of the safe zone based on pre-calculated safe boundaries
coord_bound_curved_inf, coord_bound_curved_sup = image_centerline_pad.transfo_phys2pix(
[[0, 0, bound_curved[0]]]), image_centerline_pad.transfo_phys2pix(
[[0, 0, bound_curved[1]]])
coord_bound_straight_inf, coord_bound_straight_sup = image_centerline_straight.transfo_phys2pix(
[[0, 0,
bound_straight[0]]]), image_centerline_straight.transfo_phys2pix(
[[0, 0, bound_straight[1]]])
if radius_safe > 0:
data_warp_curved2straight[:, :, 0:coord_bound_straight_inf[0][2],
0, :] = 100000.0
data_warp_curved2straight[:, :, coord_bound_straight_sup[0][2]:,
0, :] = 100000.0
data_warp_straight2curved[:, :, 0:coord_bound_curved_inf[0][2],
0, :] = 100000.0
data_warp_straight2curved[:, :, coord_bound_curved_sup[0][2]:,
0, :] = 100000.0
# Generate warp files as a warping fields
hdr_warp_s.set_intent('vector', (), '')
hdr_warp_s.set_data_dtype('float32')
hdr_warp.set_intent('vector', (), '')
hdr_warp.set_data_dtype('float32')
if self.curved2straight:
img = Nifti1Image(data_warp_curved2straight, None, hdr_warp_s)
save(img, 'tmp.curve2straight.nii.gz')
logger.info('Warping field generated: tmp.curve2straight.nii.gz')
if self.straight2curved:
img = Nifti1Image(data_warp_straight2curved, None, hdr_warp)
save(img, 'tmp.straight2curve.nii.gz')
logger.info('Warping field generated: tmp.straight2curve.nii.gz')
image_centerline_straight.save(fname_ref)
if self.curved2straight:
logger.info('Apply transformation to input image...')
run_proc([
'isct_antsApplyTransforms', '-d', '3', '-r', fname_ref, '-i',
'data.nii', '-o', 'tmp.anat_rigid_warp.nii.gz', '-t',
'tmp.curve2straight.nii.gz', '-n', 'BSpline[3]'
],
is_sct_binary=True,
verbose=verbose)
if self.accuracy_results:
time_accuracy_results = time.time()
# compute the error between the straightened centerline/segmentation and the central vertical line.
# Ideally, the error should be zero.
# Apply deformation to input image
logger.info('Apply transformation to centerline image...')
run_proc([
'isct_antsApplyTransforms', '-d', '3', '-r', fname_ref, '-i',
'centerline.nii.gz', '-o', 'tmp.centerline_straight.nii.gz',
'-t', 'tmp.curve2straight.nii.gz', '-n', 'NearestNeighbor'
],
is_sct_binary=True,
verbose=verbose)
file_centerline_straight = Image('tmp.centerline_straight.nii.gz',
verbose=verbose)
nx, ny, nz, nt, px, py, pz, pt = file_centerline_straight.dim
coordinates_centerline = file_centerline_straight.getNonZeroCoordinates(
sorting='z')
mean_coord = []
for z in range(coordinates_centerline[0].z,
coordinates_centerline[-1].z):
temp_mean = [
coord.value for coord in coordinates_centerline
if coord.z == z
]
if temp_mean:
mean_value = np.mean(temp_mean)
mean_coord.append(
np.mean([[
coord.x * coord.value / mean_value,
coord.y * coord.value / mean_value
] for coord in coordinates_centerline if coord.z == z],
axis=0))
# compute error between the straightened centerline and the straight line.
x0 = file_centerline_straight.data.shape[0] / 2.0
y0 = file_centerline_straight.data.shape[1] / 2.0
count_mean = 0
if number_of_points >= 10:
mean_c = mean_coord[
2:
-2] # we don't include the four extrema because there are usually messy.
else:
mean_c = mean_coord
for coord_z in mean_c:
if not np.isnan(np.sum(coord_z)):
dist = ((x0 - coord_z[0]) * px)**2 + (
(y0 - coord_z[1]) * py)**2
self.mse_straightening += dist
dist = np.sqrt(dist)
if dist > self.max_distance_straightening:
self.max_distance_straightening = dist
count_mean += 1
self.mse_straightening = np.sqrt(self.mse_straightening /
float(count_mean))
self.elapsed_time_accuracy = time.time() - time_accuracy_results
os.chdir(curdir)
# Generate output file (in current folder)
# TODO: do not uncompress the warping field, it is too time consuming!
logger.info('Generate output files...')
if self.curved2straight:
sct.generate_output_file(
os.path.join(path_tmp, "tmp.curve2straight.nii.gz"),
os.path.join(self.path_output, "warp_curve2straight.nii.gz"),
verbose)
if self.straight2curved:
sct.generate_output_file(
os.path.join(path_tmp, "tmp.straight2curve.nii.gz"),
os.path.join(self.path_output, "warp_straight2curve.nii.gz"),
verbose)
# create ref_straight.nii.gz file that can be used by other SCT functions that need a straight reference space
if self.curved2straight:
sct.copy(os.path.join(path_tmp, "tmp.anat_rigid_warp.nii.gz"),
os.path.join(self.path_output, "straight_ref.nii.gz"))
# move straightened input file
if fname_output == '':
fname_straight = sct.generate_output_file(
os.path.join(path_tmp, "tmp.anat_rigid_warp.nii.gz"),
os.path.join(self.path_output,
file_anat + "_straight" + ext_anat), verbose)
else:
fname_straight = sct.generate_output_file(
os.path.join(path_tmp, "tmp.anat_rigid_warp.nii.gz"),
os.path.join(self.path_output, fname_output),
verbose) # straightened anatomic
# Remove temporary files
if remove_temp_files:
logger.info('Remove temporary files...')
sct.rmtree(path_tmp)
if self.accuracy_results:
logger.info('Maximum x-y error: {} mm'.format(
self.max_distance_straightening))
logger.info('Accuracy of straightening (MSE): {} mm'.format(
self.mse_straightening))
# display elapsed time
self.elapsed_time = int(np.round(time.time() - start_time))
return fname_straight
def register(param, file_src, file_dest, file_mat, file_out, im_mask=None):
"""
Register two images by estimating slice-wise Tx and Ty transformations, which are regularized along Z. This function
uses ANTs' isct_antsSliceRegularizedRegistration.
:param param:
:param file_src:
:param file_dest:
:param file_mat:
:param file_out:
:param im_mask: Image of mask, could be 2D or 3D
:return:
"""
# TODO: deal with mask
# initialization
failed_transfo = 0 # by default, failed matrix is 0 (i.e., no failure)
do_registration = True
# get metric radius (if MeanSquares, CC) or nb bins (if MI)
if param.metric == 'MI':
metric_radius = '16'
else:
metric_radius = '4'
file_out_concat = file_out
kw = dict()
im_data = Image(
file_src
) # TODO: pass argument to use antsReg instead of opening Image each time
# register file_src to file_dest
if param.todo == 'estimate' or param.todo == 'estimate_and_apply':
# If orientation is sagittal, use antsRegistration in 2D mode
# Note: the parameter --restrict-deformation is irrelevant with affine transfo
if param.sampling == 'None':
# 'None' sampling means 'fully dense' sampling
# see https://github.com/ANTsX/ANTs/wiki/antsRegistration-reproducibility-issues
sampling = param.sampling
else:
# param.sampling should be a float in [0,1], and means the
# samplingPercentage that chooses a subset of points to
# estimate from. We always use 'Regular' (evenly-spaced)
# mode, though antsRegistration offers 'Random' as well.
# Be aware: even 'Regular' is not fully deterministic:
# > Regular includes a random perturbation on the grid sampling
# - https://github.com/ANTsX/ANTs/issues/976#issuecomment-602313884
sampling = 'Regular,' + param.sampling
if im_data.orientation[2] in 'LR':
cmd = [
'isct_antsRegistration', '-d', '2', '--transform',
'Affine[%s]' % param.gradStep, '--metric',
param.metric + '[' + file_dest + ',' + file_src + ',1,' +
metric_radius + ',' + sampling + ']', '--convergence',
param.iter, '--shrink-factors', '1', '--smoothing-sigmas',
param.smooth, '--verbose', '1', '--output',
'[' + file_mat + ',' + file_out_concat + ']'
]
cmd += sct.get_interpolation('isct_antsRegistration', param.interp)
if im_mask is not None:
# if user specified a mask, make sure there are non-null voxels in the image before running the registration
if np.count_nonzero(im_mask.data):
cmd += ['--masks', im_mask.absolutepath]
else:
# Mask only contains zeros. Copying the image instead of estimating registration.
sct.copy(file_src, file_out_concat, verbose=0)
do_registration = False
# TODO: create affine mat file with identity, in case used by -g 2
# 3D mode
else:
cmd = [
'isct_antsSliceRegularizedRegistration', '--polydegree',
param.poly, '--transform',
'Translation[%s]' % param.gradStep, '--metric',
param.metric + '[' + file_dest + ',' + file_src + ',1,' +
metric_radius + ',' + sampling + ']', '--iterations',
param.iter, '--shrinkFactors', '1', '--smoothingSigmas',
param.smooth, '--verbose', '1', '--output',
'[' + file_mat + ',' + file_out_concat + ']'
]
cmd += sct.get_interpolation(
'isct_antsSliceRegularizedRegistration', param.interp)
if im_mask is not None:
cmd += ['--mask', im_mask.absolutepath]
# run command
if do_registration:
kw.update(dict(is_sct_binary=True))
# reducing the number of CPU used for moco (see issue #201 and #2642)
env = {
**os.environ,
**{
"ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS": "1"
}
}
status, output = run_proc(cmd,
verbose=1 if param.verbose == 2 else 0,
env=env,
**kw)
elif param.todo == 'apply':
sct_apply_transfo.main(args=[
'-i', file_src, '-d', file_dest, '-w', file_mat + param.suffix_mat,
'-o', file_out_concat, '-x', param.interp, '-v', '0'
])
# check if output file exists
# Note (from JCA): In the past, i've tried to catch non-zero output from ANTs function (via the 'status' variable),
# but in some OSs, the function can fail while outputing zero. So as a pragmatic approach, I decided to go with
# the "output file checking" approach, which is 100% sensitive.
if not os.path.isfile(file_out_concat):
# sct.printv(output, verbose, 'error')
sct.printv(
'WARNING in ' + os.path.basename(__file__) +
': No output. Maybe related to improper calculation of '
'mutual information. Either the mask you provided is '
'too small, or the subject moved a lot. If you see too '
'many messages like this try with a bigger mask. '
'Using previous transformation for this volume (if it'
'exists).', param.verbose, 'warning')
failed_transfo = 1
# If sagittal, copy header (because ANTs screws it) and add singleton in 3rd dimension (for z-concatenation)
if im_data.orientation[2] in 'LR' and do_registration:
im_out = Image(file_out_concat)
im_out.header = im_data.header
im_out.data = np.expand_dims(im_out.data, 2)
im_out.save(file_out, verbose=0)
# return status of failure
return failed_transfo
def main(args=None):
# initialize parameters
param = Param()
# call main function
parser = get_parser()
if args:
arguments = parser.parse_args(args)
else:
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_data = arguments.i
fname_bvecs = arguments.bvec
average = arguments.a
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = arguments.r
path_out = arguments.ofolder
fname_bvals = arguments.bval
if arguments.bvalmin:
param.bval_min = arguments.bvalmin
# Initialization
start_time = time.time()
# sct.printv(arguments)
sct.printv('\nInput parameters:', verbose)
sct.printv(' input file ............' + fname_data, verbose)
sct.printv(' bvecs file ............' + fname_bvecs, verbose)
sct.printv(' bvals file ............' + fname_bvals, verbose)
sct.printv(' average ...............' + str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# create temporary folder
path_tmp = sct.tmp_create(basename="dmri_separate", verbose=verbose)
# copy files into tmp folder and convert to nifti
sct.printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = file_data + '_b0'
b0_mean_name = b0_name + '_mean'
dwi_name = file_data + '_dwi'
dwi_mean_name = dwi_name + '_mean'
if not convert(fname_data, os.path.join(path_tmp, dmri_name + ext)):
sct.printv('ERROR in convert.', 1, 'error')
sct.copy(fname_bvecs, os.path.join(path_tmp, "bvecs"), verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name + ext)
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# Identify b=0 and DWI images
sct.printv(fname_bvals)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals, param.bval_min, verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', verbose)
from sct_image import concat_data
l = []
for it in range(nb_b0):
l.append(dmri_name + '_T' + str(index_b0[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(b0_name + ext)
# Average b=0 images
if average:
sct.printv('\nAverage b=0...', verbose)
run_proc(['sct_maths', '-i', b0_name + ext, '-o', b0_mean_name + ext, '-mean', 't'], verbose)
# Merge DWI
l = []
for it in range(nb_dwi):
l.append(dmri_name + '_T' + str(index_dwi[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(dwi_name + ext)
# Average DWI images
if average:
sct.printv('\nAverage DWI...', verbose)
run_proc(['sct_maths', '-i', dwi_name + ext, '-o', dwi_mean_name + ext, '-mean', 't'], verbose)
# come back
os.chdir(curdir)
# Generate output files
fname_b0 = os.path.abspath(os.path.join(path_out, b0_name + ext_data))
fname_dwi = os.path.abspath(os.path.join(path_out, dwi_name + ext_data))
fname_b0_mean = os.path.abspath(os.path.join(path_out, b0_mean_name + ext_data))
fname_dwi_mean = os.path.abspath(os.path.join(path_out, dwi_mean_name + ext_data))
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, b0_name + ext), fname_b0, verbose=verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_name + ext), fname_dwi, verbose=verbose)
if average:
sct.generate_output_file(os.path.join(path_tmp, b0_mean_name + ext), fname_b0_mean, verbose=verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_mean_name + ext), fname_dwi_mean, verbose=verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove 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)
return fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean
def propseg(img_input, options_dict):
"""
:param img_input: source image, to be segmented
:param options_dict: arguments as dictionary
:return: segmented Image
"""
arguments = options_dict
fname_input_data = img_input.absolutepath
fname_data = os.path.abspath(fname_input_data)
contrast_type = arguments.c
contrast_type_conversion = {
't1': 't1',
't2': 't2',
't2s': 't2',
'dwi': 't1'
}
contrast_type_propseg = contrast_type_conversion[contrast_type]
# Starting building the command
cmd = ['isct_propseg', '-t', contrast_type_propseg]
if arguments.ofolder is not None:
folder_output = arguments.ofolder
else:
folder_output = './'
cmd += ['-o', folder_output]
if not os.path.isdir(folder_output) and os.path.exists(folder_output):
logger.error("output directory %s is not a valid directory" %
folder_output)
if not os.path.exists(folder_output):
os.makedirs(folder_output)
if arguments.down is not None:
cmd += ["-down", str(arguments.down)]
if arguments.up is not None:
cmd += ["-up", str(arguments.up)]
remove_temp_files = arguments.r
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
# Update for propseg binary
if verbose > 0:
cmd += ["-verbose"]
# Output options
if arguments.mesh is not None:
cmd += ["-mesh"]
if arguments.centerline_binary is not None:
cmd += ["-centerline-binary"]
if arguments.CSF is not None:
cmd += ["-CSF"]
if arguments.centerline_coord is not None:
cmd += ["-centerline-coord"]
if arguments.cross is not None:
cmd += ["-cross"]
if arguments.init_tube is not None:
cmd += ["-init-tube"]
if arguments.low_resolution_mesh is not None:
cmd += ["-low-resolution-mesh"]
# TODO: Not present. Why is this here? Was this renamed?
# if arguments.detect_nii is not None:
# cmd += ["-detect-nii"]
# TODO: Not present. Why is this here? Was this renamed?
# if arguments.detect_png is not None:
# cmd += ["-detect-png"]
# Helping options
use_viewer = None
use_optic = True # enabled by default
init_option = None
rescale_header = arguments.rescale
if arguments.init is not None:
init_option = float(arguments.init)
if init_option < 0:
sct.printv(
'Command-line usage error: ' + str(init_option) +
" is not a valid value for '-init'", 1, 'error')
sys.exit(1)
if arguments.init_centerline is not None:
if str(arguments.init_centerline) == "viewer":
use_viewer = "centerline"
elif str(arguments.init_centerline) == "hough":
use_optic = False
else:
if rescale_header is not 1:
fname_labels_viewer = func_rescale_header(str(
arguments.init_centerline),
rescale_header,
verbose=verbose)
else:
fname_labels_viewer = str(arguments.init_centerline)
cmd += ["-init-centerline", fname_labels_viewer]
use_optic = False
if arguments.init_mask is not None:
if str(arguments.init_mask) == "viewer":
use_viewer = "mask"
else:
if rescale_header is not 1:
fname_labels_viewer = func_rescale_header(
str(arguments.init_mask), rescale_header)
else:
fname_labels_viewer = str(arguments.init_mask)
cmd += ["-init-mask", fname_labels_viewer]
use_optic = False
if arguments.mask_correction is not None:
cmd += ["-mask-correction", str(arguments.mask_correction)]
if arguments.radius is not None:
cmd += ["-radius", str(arguments.radius)]
# TODO: Not present. Why is this here? Was this renamed?
# if arguments.detect_n is not None:
# cmd += ["-detect-n", str(arguments.detect_n)]
# TODO: Not present. Why is this here? Was this renamed?
# if arguments.detect_gap is not None:
# cmd += ["-detect-gap", str(arguments.detect_gap)]
# TODO: Not present. Why is this here? Was this renamed?
# if arguments.init_validation is not None:
# cmd += ["-init-validation"]
if arguments.nbiter is not None:
cmd += ["-nbiter", str(arguments.nbiter)]
if arguments.max_area is not None:
cmd += ["-max-area", str(arguments.max_area)]
if arguments.max_deformation is not None:
cmd += ["-max-deformation", str(arguments.max_deformation)]
if arguments.min_contrast is not None:
cmd += ["-min-contrast", str(arguments.min_contrast)]
if arguments.d is not None:
cmd += ["-d", str(arguments["-d"])]
if arguments.distance_search is not None:
cmd += ["-dsearch", str(arguments.distance_search)]
if arguments.alpha is not None:
cmd += ["-alpha", str(arguments.alpha)]
# check if input image is in 3D. Otherwise itk image reader will cut the 4D image in 3D volumes and only take the first one.
image_input = Image(fname_data)
image_input_rpi = image_input.copy().change_orientation('RPI')
nx, ny, nz, nt, px, py, pz, pt = image_input_rpi.dim
if nt > 1:
sct.printv(
'ERROR: your input image needs to be 3D in order to be segmented.',
1, 'error')
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_tmp = sct.tmp_create(basename="label_vertebrae", verbose=verbose)
# rescale header (see issue #1406)
if rescale_header is not 1:
fname_data_propseg = func_rescale_header(fname_data, rescale_header)
else:
fname_data_propseg = fname_data
# add to command
cmd += ['-i', fname_data_propseg]
# if centerline or mask is asked using viewer
if use_viewer:
from spinalcordtoolbox.gui.base import AnatomicalParams
from spinalcordtoolbox.gui.centerline import launch_centerline_dialog
params = AnatomicalParams()
if use_viewer == 'mask':
params.num_points = 3
params.interval_in_mm = 15 # superior-inferior interval between two consecutive labels
params.starting_slice = 'midfovminusinterval'
if use_viewer == 'centerline':
# setting maximum number of points to a reasonable value
params.num_points = 20
params.interval_in_mm = 30
params.starting_slice = 'top'
im_data = Image(fname_data_propseg)
im_mask_viewer = msct_image.zeros_like(im_data)
# im_mask_viewer.absolutepath = sct.add_suffix(fname_data_propseg, '_labels_viewer')
controller = launch_centerline_dialog(im_data, im_mask_viewer, params)
fname_labels_viewer = sct.add_suffix(fname_data_propseg,
'_labels_viewer')
if not controller.saved:
sct.printv(
'The viewer has been closed before entering all manual points. Please try again.',
1, 'error')
sys.exit(1)
# save labels
controller.as_niftii(fname_labels_viewer)
# add mask filename to parameters string
if use_viewer == "centerline":
cmd += ["-init-centerline", fname_labels_viewer]
elif use_viewer == "mask":
cmd += ["-init-mask", fname_labels_viewer]
# If using OptiC
elif use_optic:
image_centerline = optic.detect_centerline(image_input, contrast_type,
verbose)
fname_centerline_optic = os.path.join(path_tmp,
'centerline_optic.nii.gz')
image_centerline.save(fname_centerline_optic)
cmd += ["-init-centerline", fname_centerline_optic]
if init_option is not None:
if init_option > 1:
init_option /= (nz - 1)
cmd += ['-init', str(init_option)]
# enabling centerline extraction by default (needed by check_and_correct_segmentation() )
cmd += ['-centerline-binary']
# run propseg
status, output = run_proc(cmd,
verbose,
raise_exception=False,
is_sct_binary=True)
# check status is not 0
if not status == 0:
sct.printv(
'Automatic cord detection failed. Please initialize using -init-centerline or -init-mask (see help)',
1, 'error')
sys.exit(1)
# build output filename
fname_seg = os.path.join(
folder_output, os.path.basename(sct.add_suffix(fname_data, "_seg")))
fname_centerline = os.path.join(
folder_output,
os.path.basename(sct.add_suffix(fname_data, "_centerline")))
# in case header was rescaled, we need to update the output file names by removing the "_rescaled"
if rescale_header is not 1:
sct.mv(
os.path.join(
folder_output,
sct.add_suffix(os.path.basename(fname_data_propseg), "_seg")),
fname_seg)
sct.mv(
os.path.join(
folder_output,
sct.add_suffix(os.path.basename(fname_data_propseg),
"_centerline")), fname_centerline)
# if user was used, copy the labelled points to the output folder (they will then be scaled back)
if use_viewer:
fname_labels_viewer_new = os.path.join(
folder_output,
os.path.basename(sct.add_suffix(fname_data, "_labels_viewer")))
sct.copy(fname_labels_viewer, fname_labels_viewer_new)
# update variable (used later)
fname_labels_viewer = fname_labels_viewer_new
# check consistency of segmentation
if arguments.correct_seg:
check_and_correct_segmentation(fname_seg,
fname_centerline,
folder_output=folder_output,
threshold_distance=3.0,
remove_temp_files=remove_temp_files,
verbose=verbose)
# copy header from input to segmentation to make sure qform is the same
sct.printv(
"Copy header input --> output(s) to make sure qform is the same.",
verbose)
list_fname = [fname_seg, fname_centerline]
if use_viewer:
list_fname.append(fname_labels_viewer)
for fname in list_fname:
im = Image(fname)
im.header = image_input.header
im.save(dtype='int8'
) # they are all binary masks hence fine to save as int8
return Image(fname_seg)
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
list_warp = self.list_warp # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
islabel = False
if self.interp == 'label':
islabel = True
self.interp = 'nn'
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# 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(self.list_warp):
# Check if this transformation should be inverted
if path_warp in self.list_warpinv:
use_inverse.append('-i')
# list_warp[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[use_inverse[idx_warp], list_warp[idx_warp]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = list_warp[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and Image(list_warp[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
# sct.check_dim(fname_dest, dim_lst=[3]) # PR 2598: we decided to skip this line.
# N.B. 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)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# 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)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
# if labels, dilate before resampling
if islabel:
sct.printv("\nDilate labels before warping...")
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
fname_dilated_labels = os.path.join(path_tmp, "dilated_data.nii")
# dilate points
dilate(Image(fname_src), 2, 'ball').save(fname_dilated_labels)
fname_src = fname_dilated_labels
sct.printv("\nApply transformation and resample to destination space...", verbose)
run_proc(['isct_antsApplyTransforms',
'-d', dim,
'-i', fname_src,
'-o', fname_out,
'-t'
] + fname_warp_list_invert + ['-r', fname_dest] + interp, verbose=verbose, is_sct_binary=True)
# if 4d, loop across the T dimension
else:
if islabel:
raise NotImplementedError
dim = '4'
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
# convert to nifti into temp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in list_warp:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp, os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# 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'
status, output = run_proc(['isct_antsApplyTransforms',
'-d', '3',
'-i', file_data_split,
'-o', file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r', file_dest + ext_dest,
] + interp, verbose, is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# Copy affine matrix from destination space to make sure qform/sform are the same
sct.printv("Copy affine matrix from destination space to make sure qform/sform are the same.", verbose)
im_src_reg = Image(fname_out)
im_src_reg.copy_qform_from_ref(Image(fname_dest))
im_src_reg.save(verbose=0) # set verbose=0 to avoid warning message about rewriting file
if islabel:
sct.printv("\nTake the center of mass of each registered dilated labels...")
labeled_img = cubic_to_point(im_src_reg)
labeled_img.save(path=fname_out)
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# Crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# If the last transformation is not an affine transfo, we need to compute the matrix space of the concatenated
# warping field
if not isLastAffine and crop_reference in [1, 2]:
sct.printv('Last transformation is not affine.')
if crop_reference in [1, 2]:
# Extract only the first ndim of the warping field
img_warp = Image(warping_field)
if dim == '2':
img_warp_ndim = Image(img_src.data[:, :], hdr=img_warp.hdr)
elif dim in ['3', '4']:
img_warp_ndim = Image(img_src.data[:, :, :], hdr=img_warp.hdr)
# Set zero to everything outside the warping field
cropper = ImageCropper(Image(fname_out))
cropper.get_bbox_from_ref(img_warp_ndim)
if crop_reference == 1:
sct.printv('Cropping strategy is: keep same matrix size, put 0 everywhere around warping field')
img_out = cropper.crop(background=0)
elif crop_reference == 2:
sct.printv('Cropping strategy is: crop around warping field (the size of warping field will '
'change)')
img_out = cropper.crop()
img_out.save(fname_out)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def test_function(param_test):
"""
Parameters
----------
file_testing
Returns
-------
path_output str: path where to output testing data
"""
# load modules of function to test
module_function_to_test = importlib.import_module(
param_test.function_to_test)
module_testing = importlib.import_module('test_' +
param_test.function_to_test)
# retrieve subject name
subject_folder = os.path.basename(param_test.path_data)
# build path_output variable
path_testing = os.getcwd()
# if not param_test.path_output:
# param_test.path_output = tmp_create(basename=(param_test.function_to_test + '_' + subject_folder), verbose=0)
# elif not os.path.isdir(param_test.path_output):
# os.makedirs(param_test.path_output)
# # get parser information
# parser = module_function_to_test.get_parser()
# if '-ofolder' in parser.options and '-ofolder' not in param_test.args:
# param_test.args += " -ofolder " + param_test.path_output
#
# dict_args = parser.parse(shlex.split(param_test.args), check_file_exist=False)
# # TODO: if file in list does not exist, raise exception and assign status=200
# # add data path to each input argument
# dict_args_with_path = parser.add_path_to_file(copy.deepcopy(dict_args), param_test.path_data, input_file=True)
# # add data path to each output argument
# dict_args_with_path = parser.add_path_to_file(copy.deepcopy(dict_args_with_path), param_test.path_output, input_file=False, output_file=True)
# # save into class
# param_test.dict_args_with_path = dict_args_with_path
# param_test.args_with_path = parser.dictionary_to_string(dict_args_with_path)
#
# initialize panda dataframe
param_test.results = DataFrame(index=[subject_folder],
data={
'status': 0,
'duration': 0,
'output': '',
'path_data': param_test.path_data,
'path_output': param_test.path_output
})
#
# # retrieve input file (will be used later for integrity testing)00
# if '-i' in dict_args:
# # check if list in case of multiple input files
# if not isinstance(dict_args_with_path['-i'], list):
# list_file_to_check = [dict_args_with_path['-i']]
# # assign field file_input for integrity testing
# param_test.file_input = dict_args['-i'].split('/')[-1]
# # update index of dataframe by appending file name for more clarity
# param_test.results = param_test.results.rename({subject_folder: os.path.join(subject_folder, dict_args['-i'])})
# else:
# list_file_to_check = dict_args_with_path['-i']
# # TODO: assign field file_input for integrity testing
# for file_to_check in list_file_to_check:
# # file_input = file_to_check.split('/')[1]
# # Check if input files exist
# if not (os.path.isfile(file_to_check)):
# param_test.status = 200
# param_test.output += '\nERROR: This input file does not exist: ' + file_to_check
# return update_param(param_test)
#
# # retrieve ground truth (will be used later for integrity testing)
# if '-igt' in dict_args:
# param_test.fname_gt = dict_args_with_path['-igt']
# # Check if ground truth files exist
# if not os.path.isfile(param_test.fname_gt):
# param_test.status = 201
# param_test.output += '\nERROR: The following file used for ground truth does not exist: ' + param_test.fname_gt
# return update_param(param_test)
# run command
cmd = ' '.join([param_test.function_to_test, param_test.args])
# param_test.output += '\nWill run in %s:' % (os.path.join(path_testing, param_test.path_output))
param_test.output += '\n====================================================================================================\n' + cmd + '\n====================================================================================================\n\n' # copy command
time_start = time.time()
try:
# os.chdir(param_test.path_output)
# if not os.path.exists(param_test.path_output):
# # in case of relative path, we want a subfolder too
# os.makedirs(param_test.path_output)
# os.chdir(path_testing)
param_test.status, o = run_proc(cmd, verbose=0)
if param_test.status:
raise Exception
except Exception as err:
param_test.status = 1
param_test.output += str(err)
return update_param(param_test)
param_test.output += o
param_test.results['duration'] = time.time() - time_start
# test integrity
if param_test.test_integrity:
param_test.output += '\n\n====================================================================================================\n' + 'INTEGRITY TESTING' + '\n====================================================================================================\n\n' # copy command
try:
# os.chdir(param_test.path_output)
param_test = module_testing.test_integrity(param_test)
# os.chdir(path_testing)
except Exception as err:
# os.chdir(path_testing)
param_test.status = 2
param_test.output += str(err)
return update_param(param_test)
return update_param(param_test)
def main(args=None):
# initializations
initz = ''
initcenter = ''
fname_initlabel = ''
file_labelz = 'labelz.nii.gz'
param = Param()
# check user arguments
parser = get_parser()
if args:
arguments = parser.parse_args(args)
else:
arguments = parser.parse_args(
args=None if sys.argv[1:] else ['--help'])
fname_in = os.path.abspath(arguments.i)
fname_seg = os.path.abspath(arguments.s)
contrast = arguments.c
path_template = os.path.abspath(arguments.t)
scale_dist = arguments.scale_dist
path_output = arguments.ofolder
param.path_qc = arguments.qc
if arguments.discfile is not None:
fname_disc = os.path.abspath(arguments.discfile)
else:
fname_disc = None
if arguments.initz is not None:
initz = arguments.initz
if arguments.initcenter is not None:
initcenter = arguments.initcenter
# if user provided text file, parse and overwrite arguments
if arguments.initfile is not None:
file = open(arguments.initfile, 'r')
initfile = ' ' + file.read().replace('\n', '')
arg_initfile = initfile.split(' ')
for idx_arg, arg in enumerate(arg_initfile):
if arg == '-initz':
initz = [int(x) for x in arg_initfile[idx_arg + 1].split(',')]
if arg == '-initcenter':
initcenter = int(arg_initfile[idx_arg + 1])
if arguments.initlabel is not None:
# get absolute path of label
fname_initlabel = os.path.abspath(arguments.initlabel)
if arguments.param is not None:
param.update(arguments.param[0])
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = arguments.r
denoise = arguments.denoise
laplacian = arguments.laplacian
path_tmp = sct.tmp_create(basename="label_vertebrae", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder...', verbose)
Image(fname_in).save(os.path.join(path_tmp, "data.nii"))
Image(fname_seg).save(os.path.join(path_tmp, "segmentation.nii"))
# Go go temp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Straighten spinal cord
sct.printv('\nStraighten spinal cord...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
cache_sig = sct.cache_signature(input_files=[fname_in, fname_seg], )
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(
os.path.join(
curdir, "warp_curve2straight.nii.gz")) and os.path.isfile(
os.path.join(
curdir,
"warp_straight2curve.nii.gz")) and os.path.isfile(
os.path.join(curdir, "straight_ref.nii.gz")):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(os.path.join(curdir, "warp_curve2straight.nii.gz"),
'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, "warp_straight2curve.nii.gz"),
'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, "straight_ref.nii.gz"),
'straight_ref.nii.gz')
# apply straightening
s, o = run_proc([
'sct_apply_transfo', '-i', 'data.nii', '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o',
'data_straight.nii'
])
else:
sct_straighten_spinalcord.main(args=[
'-i',
'data.nii',
'-s',
'segmentation.nii',
'-r',
str(remove_temp_files),
'-v',
str(verbose),
])
sct.cache_save(cachefile, cache_sig)
# resample to 0.5mm isotropic to match template resolution
sct.printv('\nResample to 0.5mm isotropic...', verbose)
s, o = run_proc([
'sct_resample', '-i', 'data_straight.nii', '-mm', '0.5x0.5x0.5', '-x',
'linear', '-o', 'data_straightr.nii'
],
verbose=verbose)
# Apply straightening to segmentation
# N.B. Output is RPI
sct.printv('\nApply straightening to segmentation...', verbose)
run_proc(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
('segmentation.nii', 'data_straightr.nii',
'warp_curve2straight.nii.gz', 'segmentation_straight.nii', 'Linear'),
verbose=verbose,
is_sct_binary=True,
)
# Threshold segmentation at 0.5
run_proc([
'sct_maths', '-i', 'segmentation_straight.nii', '-thr', '0.5', '-o',
'segmentation_straight.nii'
], verbose)
# If disc label file is provided, label vertebrae using that file instead of automatically
if fname_disc:
# Apply straightening to disc-label
sct.printv('\nApply straightening to disc labels...', verbose)
run_proc(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(fname_disc, 'data_straightr.nii', 'warp_curve2straight.nii.gz',
'labeldisc_straight.nii.gz', 'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
label_vert('segmentation_straight.nii',
'labeldisc_straight.nii.gz',
verbose=1)
else:
# create label to identify disc
sct.printv('\nCreate label to identify disc...', verbose)
fname_labelz = os.path.join(path_tmp, file_labelz)
if initz or initcenter:
if initcenter:
# find z centered in FOV
nii = Image('segmentation.nii').change_orientation("RPI")
nx, ny, nz, nt, px, py, pz, pt = nii.dim # Get dimensions
z_center = int(np.round(nz / 2)) # get z_center
initz = [z_center, initcenter]
im_label = create_labels_along_segmentation(
Image('segmentation.nii'), [(initz[0], initz[1])])
im_label.data = dilate(im_label.data, 3, 'ball')
im_label.save(fname_labelz)
elif fname_initlabel:
Image(fname_initlabel).save(fname_labelz)
else:
# automatically finds C2-C3 disc
im_data = Image('data.nii')
im_seg = Image('segmentation.nii')
if not remove_temp_files: # because verbose is here also used for keeping temp files
verbose_detect_c2c3 = 2
else:
verbose_detect_c2c3 = 0
im_label_c2c3 = detect_c2c3(im_data,
im_seg,
contrast,
verbose=verbose_detect_c2c3)
ind_label = np.where(im_label_c2c3.data)
if not np.size(ind_label) == 0:
im_label_c2c3.data[ind_label] = 3
else:
sct.printv(
'Automatic C2-C3 detection failed. Please provide manual label with sct_label_utils',
1, 'error')
sys.exit()
im_label_c2c3.save(fname_labelz)
# dilate label so it is not lost when applying warping
dilate(Image(fname_labelz), 3, 'ball').save(fname_labelz)
# Apply straightening to z-label
sct.printv('\nAnd apply straightening to label...', verbose)
run_proc(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(file_labelz, 'data_straightr.nii', 'warp_curve2straight.nii.gz',
'labelz_straight.nii.gz', 'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
# get z value and disk value to initialize labeling
sct.printv('\nGet z and disc values from straight label...', verbose)
init_disc = get_z_and_disc_values_from_label('labelz_straight.nii.gz')
sct.printv('.. ' + str(init_disc), verbose)
# denoise data
if denoise:
sct.printv('\nDenoise data...', verbose)
run_proc([
'sct_maths', '-i', 'data_straightr.nii', '-denoise', 'h=0.05',
'-o', 'data_straightr.nii'
], verbose)
# apply laplacian filtering
if laplacian:
sct.printv('\nApply Laplacian filter...', verbose)
run_proc([
'sct_maths', '-i', 'data_straightr.nii', '-laplacian', '1',
'-o', 'data_straightr.nii'
], verbose)
# detect vertebral levels on straight spinal cord
init_disc[1] = init_disc[1] - 1
vertebral_detection('data_straightr.nii',
'segmentation_straight.nii',
contrast,
param,
init_disc=init_disc,
verbose=verbose,
path_template=path_template,
path_output=path_output,
scale_dist=scale_dist)
# un-straighten labeled spinal cord
sct.printv('\nUn-straighten labeling...', verbose)
run_proc(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
('segmentation_straight_labeled.nii', 'segmentation.nii',
'warp_straight2curve.nii.gz', 'segmentation_labeled.nii',
'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
# Clean labeled segmentation
sct.printv(
'\nClean labeled segmentation (correct interpolation errors)...',
verbose)
clean_labeled_segmentation('segmentation_labeled.nii', 'segmentation.nii',
'segmentation_labeled.nii')
# label discs
sct.printv('\nLabel discs...', verbose)
label_discs('segmentation_labeled.nii', verbose=verbose)
# come back
os.chdir(curdir)
# Generate output files
path_seg, file_seg, ext_seg = sct.extract_fname(fname_seg)
fname_seg_labeled = os.path.join(path_output,
file_seg + '_labeled' + ext_seg)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(
os.path.join(path_tmp, "segmentation_labeled.nii"), fname_seg_labeled)
sct.generate_output_file(
os.path.join(path_tmp, "segmentation_labeled_disc.nii"),
os.path.join(path_output, file_seg + '_labeled_discs' + ext_seg))
# 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=verbose)
sct.generate_output_file(os.path.join(path_tmp,
"warp_straight2curve.nii.gz"),
os.path.join(path_output,
"warp_straight2curve.nii.gz"),
verbose=verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"),
os.path.join(path_output, "straight_ref.nii.gz"),
verbose=verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp)
# Generate QC report
if param.path_qc is not None:
path_qc = os.path.abspath(arguments.qc)
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
labeled_seg_file = os.path.join(path_output,
file_seg + '_labeled' + ext_seg)
generate_qc(fname_in,
fname_seg=labeled_seg_file,
args=args,
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
process='sct_label_vertebrae')
sct.display_viewer_syntax([fname_in, fname_seg_labeled],
colormaps=['', 'subcortical'],
opacities=['1', '0.5'])
def detect_centerline(img, contrast, verbose=1):
"""Detect spinal cord centerline using OptiC.
:param img: input Image() object.
:param contrast: str: The type of contrast. Will define the path to Optic model.
:returns: Image(): Output centerline
"""
# Fetch path to Optic model based on contrast
optic_models_path = sct_dir_local_path('data', 'optic_models',
'{}_model'.format(contrast))
logger.debug('Detecting the spinal cord using OptiC')
img_orientation = img.orientation
temp_folder = sct.TempFolder()
temp_folder.chdir()
# convert image data type to int16, as required by opencv (backend in OptiC)
img_int16 = img.copy()
# Replace non-numeric values by zero
img_data = img.data
img_data[np.where(np.isnan(img_data))] = 0
img_data[np.where(np.isinf(img_data))] = 0
img_int16.data[np.where(np.isnan(img_int16.data))] = 0
img_int16.data[np.where(np.isinf(img_int16.data))] = 0
# rescale intensity
min_out = np.iinfo('uint16').min
max_out = np.iinfo('uint16').max
min_in = np.nanmin(img_data)
max_in = np.nanmax(img_data)
data_rescaled = img_data.astype('float') * (max_out - min_out) / (max_in -
min_in)
img_int16.data = data_rescaled - (data_rescaled.min() - min_out)
# change data type
img_int16.change_type(np.uint16)
# reorient the input image to RPI + convert to .nii
img_int16.change_orientation('RPI')
file_img = 'img_rpi_uint16'
img_int16.save(file_img + '.nii')
# call the OptiC method to generate the spinal cord centerline
optic_input = file_img
optic_filename = file_img + '_optic'
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_optic = [
'isct_spine_detect',
'-ctype=dpdt',
'-lambda=1',
optic_models_path,
optic_input,
optic_filename,
]
# TODO: output coordinates, for each slice, in continuous (not discrete) values.
run_proc(cmd_optic, is_sct_binary=True, verbose=0)
# convert .img and .hdr files to .nii.gz
img_ctl = Image(file_img + '_optic_ctr.hdr')
img_ctl.change_orientation(img_orientation)
# return to initial folder
temp_folder.chdir_undo()
if verbose < 2:
logger.info("Remove temporary files...")
temp_folder.cleanup()
return img_ctl
