def apply_transfo(im_src, im_dest, warp, interp='spline', rm_tmp=True):
# create tmp dir and go in it
tmp_dir = sct.tmp_create()
# copy warping field to tmp dir
sct.copy(warp, tmp_dir)
warp = ''.join(extract_fname(warp)[1:])
# go to tmp dir
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
# apply warping field
fname_src_reg = add_suffix(fname_src, '_reg')
sct_apply_transfo.main(
args=['-i', fname_src, '-d', fname_dest, '-w', warp, '-x', interp])
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
if rm_tmp:
# remove tmp dir
sct.rmtree(tmp_dir)
# return res image
return im_src_reg
def main(args=None):
if args is None:
args = sys.argv[1:]
# Get parser
parser = get_parser()
arguments = parser.parse(args)
# Set param arguments ad inputted by user
fname_in = arguments["-i"]
contrast = arguments["-c"]
# Segmentation or Centerline line
if '-s' in arguments:
fname_seg = arguments['-s']
if not os.path.isfile(fname_seg):
fname_seg = None
sct.printv('WARNING: -s input file: "' + arguments['-s'] + '" does not exist.\nDetecting PMJ without using segmentation information', 1, 'warning')
else:
fname_seg = None
# Output Folder
if '-ofolder' in arguments:
path_results = arguments["-ofolder"]
if not os.path.isdir(path_results) and os.path.exists(path_results):
sct.printv("ERROR output directory %s is not a valid directory" % path_results, 1, 'error')
if not os.path.exists(path_results):
os.makedirs(path_results)
else:
path_results = '.'
path_qc = arguments.get("-qc", None)
# Remove temp folder
rm_tmp = bool(int(arguments.get("-r", 1)))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# Initialize DetectPMJ
detector = DetectPMJ(fname_im=fname_in,
contrast=contrast,
fname_seg=fname_seg,
path_out=path_results,
verbose=verbose)
# run the extraction
fname_out, tmp_dir = detector.apply()
# Remove tmp_dir
if rm_tmp:
sct.rmtree(tmp_dir)
# View results
if fname_out is not None:
if path_qc is not None:
generate_qc(fname_in, fname_seg=fname_out, args=args, path_qc=os.path.abspath(path_qc),
process='sct_detect_pmj')
sct.display_viewer_syntax([fname_in, fname_out], colormaps=['gray', 'red'])
def apply_transfo(im_src, im_dest, warp, interp='spline', rm_tmp=True):
# create tmp dir and go in it
tmp_dir = sct.tmp_create()
# copy warping field to tmp dir
sct.copy(warp, tmp_dir)
warp = ''.join(extract_fname(warp)[1:])
# go to tmp dir
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
# apply warping field
fname_src_reg = add_suffix(fname_src, '_reg')
sct_apply_transfo.main(args=['-i', fname_src,
'-d', fname_dest,
'-w', warp,
'-x', interp])
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
if rm_tmp:
# remove tmp dir
sct.rmtree(tmp_dir)
# return res image
return im_src_reg
def register_data(im_src, im_dest, param_reg, path_copy_warp=None, rm_tmp=True):
'''
Parameters
----------
im_src: class Image: source image
im_dest: class Image: destination image
param_reg: str: registration parameter
path_copy_warp: path: path to copy the warping fields
Returns: im_src_reg: class Image: source image registered on destination image
-------
'''
# im_src and im_dest are already preprocessed (in theory: im_dest = mean_image)
# binarize images to get seg
im_src_seg = binarize(im_src, thr_min=1, thr_max=1)
im_dest_seg = binarize(im_dest)
# create tmp dir and go in it
tmp_dir = sct.tmp_create()
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_src_seg = 'src_seg.nii.gz'
im_src_seg.save(fname_src_seg)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
fname_dest_seg = 'dest_seg.nii.gz'
im_dest_seg.save(fname_dest_seg)
# do registration using param_reg
sct_register_multimodal.main(args=['-i', fname_src,
'-d', fname_dest,
'-iseg', fname_src_seg,
'-dseg', fname_dest_seg,
'-param', param_reg])
# get registration result
fname_src_reg = add_suffix(fname_src, '_reg')
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
# copy warping fields
if path_copy_warp is not None and os.path.isdir(os.path.abspath(path_copy_warp)):
path_copy_warp = os.path.abspath(path_copy_warp)
file_src = extract_fname(fname_src)[1]
file_dest = extract_fname(fname_dest)[1]
fname_src2dest = 'warp_' + file_src + '2' + file_dest + '.nii.gz'
fname_dest2src = 'warp_' + file_dest + '2' + file_src + '.nii.gz'
sct.copy(os.path.join(tmp_dir, fname_src2dest), path_copy_warp)
sct.copy(os.path.join(tmp_dir, fname_dest2src), path_copy_warp)
if rm_tmp:
# remove tmp dir
sct.rmtree(tmp_dir)
# return res image
return im_src_reg, fname_src2dest, fname_dest2src
def main(args=None):
"""
Main function
:param args:
:return:
"""
# get parser args
if args is None:
args = None if sys.argv[1:] else ['--help']
parser = get_parser()
arguments = parser.parse_args(args=args)
# create param object
param = Param()
param_glcm = ParamGLCM()
# set param arguments ad inputted by user
param.fname_im = arguments.i
param.fname_seg = arguments.m
if arguments.ofolder is not None:
param.path_results = arguments.ofolder
if not os.path.isdir(param.path_results) and os.path.exists(
param.path_results):
sct.printv(
"ERROR output directory %s is not a valid directory" %
param.path_results, 1, 'error')
if not os.path.exists(param.path_results):
os.makedirs(param.path_results)
if arguments.feature is not None:
param_glcm.feature = arguments.feature
if arguments.distance is not None:
param_glcm.distance = arguments.distance
if arguments.angle is not None:
param_glcm.angle = arguments.angle
if arguments.dim is not None:
param.dim = arguments.dim
if arguments.r is not None:
param.rm_tmp = bool(arguments.r)
verbose = arguments.v
sct.init_sct(log_level=verbose, update=True) # Update log level
# create the GLCM constructor
glcm = ExtractGLCM(param=param, param_glcm=param_glcm)
# run the extraction
fname_out_lst = glcm.extract()
# remove tmp_dir
if param.rm_tmp:
sct.rmtree(glcm.tmp_dir)
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv(
'fsleyes ' + arguments.i + ' ' +
' -cm red-yellow -a 70.0 '.join(fname_out_lst) +
' -cm Red-Yellow -a 70.0 & \n', param.verbose, 'info')
def main(args=None):
if args is None:
args = sys.argv[1:]
# create param object
param = Param()
param_glcm = ParamGLCM()
# get parser
parser = get_parser()
arguments = parser.parse(args)
# set param arguments ad inputted by user
param.fname_im = arguments["-i"]
param.fname_seg = arguments["-m"]
if '-ofolder' in arguments:
param.path_results = arguments["-ofolder"]
if not os.path.isdir(param.path_results) and os.path.exists(
param.path_results):
sct.printv(
"ERROR output directory %s is not a valid directory" %
param.path_results, 1, 'error')
if not os.path.exists(param.path_results):
os.makedirs(param.path_results)
if '-feature' in arguments:
param_glcm.feature = arguments['-feature']
if '-distance' in arguments:
param_glcm.distance = int(arguments['-distance'])
if '-angle' in arguments:
param_glcm.angle = arguments['-angle']
if '-dim' in arguments:
param.dim = arguments['-dim']
if '-r' in arguments:
param.rm_tmp = bool(int(arguments['-r']))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# create the GLCM constructor
glcm = ExtractGLCM(param=param, param_glcm=param_glcm)
# run the extraction
fname_out_lst = glcm.extract()
# remove tmp_dir
if param.rm_tmp:
sct.rmtree(glcm.tmp_dir)
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv(
'fslview ' + arguments["-i"] + ' ' +
' -l Red-Yellow -t 0.7 '.join(fname_out_lst) +
' -l Red-Yellow -t 0.7 & \n', param.verbose, 'info')
def coregister_model_data(self):
# get mean image
im_mean = Image(param=self.mean_image)
# register all slices WM on mean WM
for dic_slice in self.slices:
# create a directory to get the warping fields
warp_dir = 'wf_slice' + str(dic_slice.id)
if not os.path.exists(warp_dir):
os.mkdir(warp_dir)
# get slice mean WM image
im_slice = Image(param=dic_slice.im)
# register slice image on mean dic image
im_slice_reg, fname_src2dest, fname_dest2src = register_data(
im_src=im_slice,
im_dest=im_mean,
param_reg=self.param_data.register_param,
path_copy_warp=warp_dir)
shape = im_slice_reg.data.shape
# use forward warping field to register all slice wm
list_wmseg_reg = []
for wm_seg in dic_slice.wm_seg:
im_wmseg = Image(param=wm_seg)
im_wmseg_reg = apply_transfo(im_src=im_wmseg,
im_dest=im_mean,
warp=os.path.join(
warp_dir, fname_src2dest),
interp='nn')
list_wmseg_reg.append(im_wmseg_reg.data.reshape(shape))
# use forward warping field to register gm seg
list_gmseg_reg = []
for gm_seg in dic_slice.gm_seg:
im_gmseg = Image(param=gm_seg)
im_gmseg_reg = apply_transfo(im_src=im_gmseg,
im_dest=im_mean,
warp=os.path.join(
warp_dir, fname_src2dest),
interp='nn')
list_gmseg_reg.append(im_gmseg_reg.data.reshape(shape))
# set slice attributes with data registered into the model space
dic_slice.set(im_m=im_slice_reg.data)
dic_slice.set(wm_seg_m=list_wmseg_reg)
dic_slice.set(gm_seg_m=list_gmseg_reg)
# remove warping fields directory
if self.param.rm_tmp:
sct.rmtree(warp_dir)
def register_slicewise(fname_src,
fname_dest,
fname_mask='',
warp_forward_out='step0Warp.nii.gz',
warp_inverse_out='step0InverseWarp.nii.gz',
paramreg=None,
ants_registration_params=None,
path_qc='./',
remove_temp_files=0,
verbose=0):
# create temporary folder
path_tmp = sct.tmp_create(basename="register", verbose=verbose)
# copy data to temp folder
sct.printv('\nCopy input data to temp folder...', verbose)
convert(fname_src, os.path.join(path_tmp, "src.nii"))
convert(fname_dest, os.path.join(path_tmp, "dest.nii"))
if fname_mask != '':
convert(fname_mask, os.path.join(path_tmp, "mask.nii.gz"))
# go to temporary folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Calculate displacement
if paramreg.algo == 'centermass':
# translation of center of mass between source and destination in voxel space
register2d_centermassrot('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, rot=0, poly=int(paramreg.poly), path_qc=path_qc, verbose=verbose)
elif paramreg.algo == 'centermassrot':
# translation of center of mass and rotation based on source and destination first eigenvectors from PCA.
register2d_centermassrot('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, rot=1, poly=int(paramreg.poly), path_qc=path_qc, verbose=verbose, pca_eigenratio_th=float(paramreg.pca_eigenratio_th))
elif paramreg.algo == 'columnwise':
# scaling R-L, then column-wise center of mass alignment and scaling
register2d_columnwise('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, verbose=verbose, path_qc=path_qc, smoothWarpXY=int(paramreg.smoothWarpXY))
else:
# convert SCT flags into ANTs-compatible flags
algo_dic = {'translation': 'Translation', 'rigid': 'Rigid', 'affine': 'Affine', 'syn': 'SyN', 'bsplinesyn': 'BSplineSyN', 'centermass': 'centermass'}
paramreg.algo = algo_dic[paramreg.algo]
# run slicewise registration
register2d('src.nii', 'dest.nii', fname_mask=fname_mask, fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, paramreg=paramreg, ants_registration_params=ants_registration_params, verbose=verbose)
sct.printv('\nMove warping fields...', verbose)
sct.copy(warp_forward_out, curdir)
sct.copy(warp_inverse_out, curdir)
# go back
os.chdir(curdir)
if remove_temp_files:
sct.rmtree(path_tmp, verbose=verbose)
def main(args=None):
if args is None:
args = sys.argv[1:]
# create param object
param = Param()
param_glcm = ParamGLCM()
# get parser
parser = get_parser()
arguments = parser.parse(args)
# set param arguments ad inputted by user
param.fname_im = arguments["-i"]
param.fname_seg = arguments["-m"]
if '-ofolder' in arguments:
param.path_results = arguments["-ofolder"]
if not os.path.isdir(param.path_results) and os.path.exists(param.path_results):
sct.printv("ERROR output directory %s is not a valid directory" % param.path_results, 1, 'error')
if not os.path.exists(param.path_results):
os.makedirs(param.path_results)
if '-feature' in arguments:
param_glcm.feature = arguments['-feature']
if '-distance' in arguments:
param_glcm.distance = int(arguments['-distance'])
if '-angle' in arguments:
param_glcm.angle = arguments['-angle']
if '-dim' in arguments:
param.dim = arguments['-dim']
if '-r' in arguments:
param.rm_tmp = bool(int(arguments['-r']))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# create the GLCM constructor
glcm = ExtractGLCM(param=param, param_glcm=param_glcm)
# run the extraction
fname_out_lst = glcm.extract()
# remove tmp_dir
if param.rm_tmp:
sct.rmtree(glcm.tmp_dir)
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview ' + arguments["-i"] + ' ' + ' -l Red-Yellow -t 0.7 '.join(fname_out_lst) + ' -l Red-Yellow -t 0.7 & \n', param.verbose, 'info')
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 = sct.run(['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')
sct.run([
'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)
sct.run([
'sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w',
fname_warp, '-o', grid_warped
])
if rm_tmp:
sct.rmtree(tmp_dir)
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)
curdir = os.getcwd()
os.chdir(tmp_dir)
assert len(im_warp.data.shape
) == 5, 'ERROR: Warping field does bot have 5 dimensions...'
nx, ny, nz, nt, ndimwarp = im_warp.data.shape
# nx, ny, nz, nt, px, py, pz, pt = im_warp.dim
# This does not work because dimensions of a warping field are not correctly read : it would be 1,1,1,1,1,1,1,1
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')
sct.run([
'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, 'grid_warped_gm' + ext_warp)
sct.run([
'sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w',
fname_warp, '-o', grid_warped
])
if rm_tmp:
sct.rmtree(tmp_dir)
return grid_warped
def coregister_model_data(self):
# get mean image
im_mean = Image(param=self.mean_image)
# register all slices WM on mean WM
for dic_slice in self.slices:
# create a directory to get the warping fields
warp_dir = 'wf_slice' + str(dic_slice.id)
if not os.path.exists(warp_dir):
os.mkdir(warp_dir)
# get slice mean WM image
im_slice = Image(param=dic_slice.im)
# register slice image on mean dic image
im_slice_reg, fname_src2dest, fname_dest2src = register_data(im_src=im_slice, im_dest=im_mean, param_reg=self.param_data.register_param, path_copy_warp=warp_dir)
shape = im_slice_reg.data.shape
# use forward warping field to register all slice wm
list_wmseg_reg = []
for wm_seg in dic_slice.wm_seg:
im_wmseg = Image(param=wm_seg)
im_wmseg_reg = apply_transfo(im_src=im_wmseg, im_dest=im_mean, warp=os.path.join(warp_dir, fname_src2dest), interp='nn')
list_wmseg_reg.append(im_wmseg_reg.data.reshape(shape))
# use forward warping field to register gm seg
list_gmseg_reg = []
for gm_seg in dic_slice.gm_seg:
im_gmseg = Image(param=gm_seg)
im_gmseg_reg = apply_transfo(im_src=im_gmseg, im_dest=im_mean, warp=os.path.join(warp_dir, fname_src2dest), interp='nn')
list_gmseg_reg.append(im_gmseg_reg.data.reshape(shape))
# set slice attributes with data registered into the model space
dic_slice.set(im_m=im_slice_reg.data)
dic_slice.set(wm_seg_m=list_wmseg_reg)
dic_slice.set(gm_seg_m=list_gmseg_reg)
# remove warping fields directory
if self.param.rm_tmp:
sct.rmtree(warp_dir)
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 = sct.run(['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')
sct.run(['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)
sct.run(['sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w', fname_warp, '-o', grid_warped])
if rm_tmp:
sct.rmtree(tmp_dir)
def validation(self):
tmp_dir_val = sct.tmp_create(basename="segment_graymatter_validation")
# copy data into tmp dir val
sct.copy(self.param_seg.fname_manual_gmseg, tmp_dir_val)
sct.copy(self.param_seg.fname_seg, tmp_dir_val)
curdir = os.getcwd()
os.chdir(tmp_dir_val)
fname_manual_gmseg = os.path.basename(self.param_seg.fname_manual_gmseg)
fname_seg = os.path.basename(self.param_seg.fname_seg)
im_gmseg = self.im_res_gmseg.copy()
im_wmseg = self.im_res_wmseg.copy()
if self.param_seg.type_seg == 'prob':
im_gmseg = binarize(im_gmseg, thr_max=0.5, thr_min=0.5)
im_wmseg = binarize(im_wmseg, thr_max=0.5, thr_min=0.5)
fname_gmseg = 'res_gmseg.nii.gz'
im_gmseg.save(fname_gmseg)
fname_wmseg = 'res_wmseg.nii.gz'
im_wmseg.save(fname_wmseg)
# get manual WM seg:
fname_manual_wmseg = 'manual_wmseg.nii.gz'
sct_maths.main(args=['-i', fname_seg,
'-sub', fname_manual_gmseg,
'-o', fname_manual_wmseg])
# compute DC:
try:
status_gm, output_gm = run('sct_dice_coefficient -i ' + fname_manual_gmseg + ' -d ' + fname_gmseg + ' -2d-slices 2')
status_wm, output_wm = run('sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' + fname_wmseg + ' -2d-slices 2')
except Exception:
# put ref and res in the same space if needed
fname_manual_gmseg_corrected = add_suffix(fname_manual_gmseg, '_reg')
sct_register_multimodal.main(args=['-i', fname_manual_gmseg,
'-d', fname_gmseg,
'-identity', '1'])
sct_maths.main(args=['-i', fname_manual_gmseg_corrected,
'-bin', '0.1',
'-o', fname_manual_gmseg_corrected])
#
fname_manual_wmseg_corrected = add_suffix(fname_manual_wmseg, '_reg')
sct_register_multimodal.main(args=['-i', fname_manual_wmseg,
'-d', fname_wmseg,
'-identity', '1'])
sct_maths.main(args=['-i', fname_manual_wmseg_corrected,
'-bin', '0.1',
'-o', fname_manual_wmseg_corrected])
# recompute DC
status_gm, output_gm = run('sct_dice_coefficient -i ' + fname_manual_gmseg_corrected + ' -d ' + fname_gmseg + ' -2d-slices 2')
status_wm, output_wm = run('sct_dice_coefficient -i ' + fname_manual_wmseg_corrected + ' -d ' + fname_wmseg + ' -2d-slices 2')
# save results to a text file
fname_dc = 'dice_coefficient_' + extract_fname(self.param_seg.fname_im)[1] + '.txt'
file_dc = open(fname_dc, 'w')
if self.param_seg.type_seg == 'prob':
file_dc.write('WARNING : the probabilistic segmentations were binarized with a threshold at 0.5 to compute the dice coefficient \n')
file_dc.write('\n--------------------------------------------------------------\nDice coefficient on the Gray Matter segmentation:\n')
file_dc.write(output_gm)
file_dc.write('\n\n--------------------------------------------------------------\nDice coefficient on the White Matter segmentation:\n')
file_dc.write(output_wm)
file_dc.close()
# compute HD and MD:
fname_hd = 'hausdorff_dist_' + extract_fname(self.param_seg.fname_im)[1] + '.txt'
run('sct_compute_hausdorff_distance -i ' + fname_gmseg + ' -d ' + fname_manual_gmseg + ' -thinning 1 -o ' + fname_hd + ' -v ' + str(self.param.verbose))
# get out of tmp dir to copy results to output folder
os.chdir(curdir)
sct.copy(os.path.join(self.tmp_dir, tmp_dir_val, fname_dc), self.param_seg.path_results)
sct.copy(os.path.join(self.tmp_dir, tmp_dir_val, fname_hd), self.param_seg.path_results)
if self.param.rm_tmp:
sct.rmtree(tmp_dir_val)
def main(args=None):
# initialization
start_time = time.time()
path_out = '.'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
param.fname_bvecs = arguments['-bvec']
if '-bval' in arguments:
param.fname_bvals = arguments['-bval']
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-thr' in arguments:
param.otsu = arguments['-thr']
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
param.fname_bvecs = os.path.abspath(param.fname_bvecs)
if param.fname_bvals != '':
param.fname_bvals = os.path.abspath(param.fname_bvals)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_mask, file_mask, ext_mask = sct.extract_fname(param.fname_mask)
path_tmp = sct.tmp_create(basename="dmri_moco", verbose=param.verbose)
# names of files in temporary folder
mask_name = 'mask'
bvecs_fname = 'bvecs.txt'
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
param.verbose)
convert(param.fname_data, os.path.join(path_tmp, "dmri.nii"))
sct.copy(param.fname_bvecs,
os.path.join(path_tmp, bvecs_fname),
verbose=param.verbose)
if param.fname_mask != '':
sct.copy(param.fname_mask,
os.path.join(path_tmp, mask_name + ext_mask),
verbose=param.verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# update field in param (because used later).
# TODO: make this cleaner...
if param.fname_mask != '':
param.fname_mask = mask_name + ext_mask
# run moco
fname_data_moco_tmp = dmri_moco(param)
# generate b0_moco_mean and dwi_moco_mean
args = [
'-i', fname_data_moco_tmp, '-bvec', 'bvecs.txt', '-a', '1', '-v', '0'
]
if not param.fname_bvals == '':
# if bvals file is provided
args += ['-bval', param.fname_bvals]
fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean = sct_dmri_separate_b0_and_dwi.main(
args=args)
# come back
os.chdir(curdir)
# Generate output files
fname_dmri_moco = os.path.join(path_out,
file_data + param.suffix + ext_data)
fname_dmri_moco_b0_mean = sct.add_suffix(fname_dmri_moco, '_b0_mean')
fname_dmri_moco_dwi_mean = sct.add_suffix(fname_dmri_moco, '_dwi_mean')
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(fname_data_moco_tmp, fname_dmri_moco,
param.verbose)
sct.generate_output_file(fname_b0_mean, fname_dmri_moco_b0_mean,
param.verbose)
sct.generate_output_file(fname_dwi_mean, fname_dmri_moco_dwi_mean,
param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's',
param.verbose)
sct.display_viewer_syntax([fname_dmri_moco, file_data], mode='ortho,ortho')
if arguments.bmax is not None and arguments.bmax == 1:
cmd += ['-bmax']
if arguments.bzmax is not None and arguments.bzmax == 1:
cmd += ['-bzmax']
if arguments.o is not None:
path_output, fname_output, ext = sct.extract_fname(arguments.o)
cmd += ['-o', fname_output + ext]
rm_tmp = bool(arguments.r)
# # Computation of Dice coefficient using Python implementation.
# # commented for now as it does not cover all the feature of isct_dice_coefficient
# #from spinalcordtoolbox.image import Image, compute_dice
# #dice = compute_dice(Image(fname_input1), Image(fname_input2), mode='3d', zboundaries=False)
# #sct.printv('Dice (python-based) = ' + str(dice), verbose)
status, output = run_proc(cmd, verbose, is_sct_binary=True)
os.chdir(curdir) # go back to original directory
# copy output file into original directory
if arguments.o is not None:
sct.copy(os.path.join(tmp_dir, fname_output + ext),
os.path.join(path_output, fname_output + ext))
# remove tmp_dir
if rm_tmp:
sct.rmtree(tmp_dir)
sct.printv(output, verbose)
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
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 msct_image.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
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# 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 = msct_image.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'
sct.run([
'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:
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 = msct_image.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 = sct.run([
'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 int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv(
'WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...',
verbose, 'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out,
output_file=fname_out,
ref=warping_field,
background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out,
output_file=fname_out,
ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def compute_properties_along_centerline(fname_seg_image,
property_list,
fname_disks_image=None,
smooth_factor=5.0,
interpolation_mode=0,
remove_temp_files=1,
verbose=1):
# Check list of properties
# If diameters is in the list, compute major and minor axis length and check orientation
compute_diameters = False
property_list_local = list(property_list)
if 'diameters' in property_list_local:
compute_diameters = True
property_list_local.remove('diameters')
property_list_local.append('major_axis_length')
property_list_local.append('minor_axis_length')
property_list_local.append('orientation')
# TODO: make sure fname_segmentation and fname_disks are in the same space
path_tmp = sct.tmp_create(basename="compute_properties_along_centerline",
verbose=verbose)
sct.copy(fname_seg_image, path_tmp)
if fname_disks_image is not None:
sct.copy(fname_disks_image, path_tmp)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
fname_segmentation = os.path.abspath(fname_seg_image)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# Change orientation of the input centerline into RPI
sct.printv('\nOrient centerline to RPI orientation...', verbose)
im_seg = Image(file_data + ext_data)
fname_segmentation_orient = 'segmentation_rpi' + ext_data
image = set_orientation(im_seg, 'RPI')
image.setFileName(fname_segmentation_orient)
image.save()
# Initiating some variables
nx, ny, nz, nt, px, py, pz, pt = image.dim
resolution = 0.5
properties = {key: [] for key in property_list_local}
properties['incremental_length'] = []
properties['distance_from_C1'] = []
properties['vertebral_level'] = []
properties['z_slice'] = []
# compute the spinal cord centerline based on the spinal cord segmentation
number_of_points = 5 * nz
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
fname_segmentation_orient,
algo_fitting='nurbs',
verbose=verbose,
nurbs_pts_number=number_of_points,
all_slices=False,
phys_coordinates=True,
remove_outliers=True)
centerline = Centerline(x_centerline_fit, y_centerline_fit, z_centerline,
x_centerline_deriv, y_centerline_deriv,
z_centerline_deriv)
# Compute vertebral distribution along centerline based on position of intervertebral disks
if fname_disks_image is not None:
fname_disks = os.path.abspath(fname_disks_image)
path_data, file_data, ext_data = sct.extract_fname(fname_disks)
im_disks = Image(file_data + ext_data)
fname_disks_orient = 'disks_rpi' + ext_data
image_disks = set_orientation(im_disks, 'RPI')
image_disks.setFileName(fname_disks_orient)
image_disks.save()
image_disks = Image(fname_disks_orient)
coord = image_disks.getNonZeroCoordinates(sorting='z',
reverse_coord=True)
coord_physical = []
for c in coord:
c_p = image_disks.transfo_pix2phys([[c.x, c.y, c.z]])[0]
c_p.append(c.value)
coord_physical.append(c_p)
centerline.compute_vertebral_distribution(coord_physical)
sct.printv('Computing spinal cord shape along the spinal cord...')
timer_properties = sct.Timer(
number_of_iteration=centerline.number_of_points)
timer_properties.start()
# Extracting patches perpendicular to the spinal cord and computing spinal cord shape
for index in range(centerline.number_of_points):
# value_out = -5.0
value_out = 0.0
current_patch = centerline.extract_perpendicular_square(
image,
index,
resolution=resolution,
interpolation_mode=interpolation_mode,
border='constant',
cval=value_out)
# check for pixels close to the spinal cord segmentation that are out of the image
from skimage.morphology import dilation
patch_zero = np.copy(current_patch)
patch_zero[patch_zero == value_out] = 0.0
patch_borders = dilation(patch_zero) - patch_zero
"""
if np.count_nonzero(patch_borders + current_patch == value_out + 1.0) != 0:
c = image.transfo_phys2pix([centerline.points[index]])[0]
print('WARNING: no patch for slice', c[2])
timer_properties.add_iteration()
continue
"""
sc_properties = properties2d(patch_zero, [resolution, resolution])
if sc_properties is not None:
properties['incremental_length'].append(
centerline.incremental_length[index])
if fname_disks_image is not None:
properties['distance_from_C1'].append(
centerline.dist_points[index])
properties['vertebral_level'].append(
centerline.l_points[index])
properties['z_slice'].append(
image.transfo_phys2pix([centerline.points[index]])[0][2])
for property_name in property_list_local:
properties[property_name].append(sc_properties[property_name])
else:
c = image.transfo_phys2pix([centerline.points[index]])[0]
print('WARNING: no properties for slice', c[2])
timer_properties.add_iteration()
timer_properties.stop()
# Adding centerline to the properties for later use
properties['centerline'] = centerline
# We assume that the major axis is in the right-left direction
# this script checks the orientation of the spinal cord and invert axis if necessary to make sure the major axis is right-left
if compute_diameters:
diameter_major = properties['major_axis_length']
diameter_minor = properties['minor_axis_length']
orientation = properties['orientation']
for i, orientation_item in enumerate(orientation):
if -45.0 < orientation_item < 45.0:
continue
else:
temp = diameter_minor[i]
properties['minor_axis_length'][i] = diameter_major[i]
properties['major_axis_length'][i] = temp
properties['RL_diameter'] = properties['major_axis_length']
properties['AP_diameter'] = properties['minor_axis_length']
del properties['major_axis_length']
del properties['minor_axis_length']
# smooth the spinal cord shape with a gaussian kernel if required
# TODO: not all properties can be smoothed
if smooth_factor != 0.0: # smooth_factor is in mm
import scipy
window = scipy.signal.hann(smooth_factor /
np.mean(centerline.progressive_length))
for property_name in property_list_local:
properties[property_name] = scipy.signal.convolve(
properties[property_name], window,
mode='same') / np.sum(window)
if compute_diameters:
property_list_local.remove('major_axis_length')
property_list_local.remove('minor_axis_length')
property_list_local.append('RL_diameter')
property_list_local.append('AP_diameter')
property_list = property_list_local
# Display properties on the referential space. Requires intervertebral disks
if verbose == 2:
x_increment = 'distance_from_C1'
if fname_disks_image is None:
x_increment = 'incremental_length'
# Display the image and plot all contours found
fig, axes = plt.subplots(len(property_list_local),
sharex=True,
sharey=False)
for k, property_name in enumerate(property_list_local):
axes[k].plot(properties[x_increment], properties[property_name])
axes[k].set_ylabel(property_name)
if fname_disks_image is not None:
properties[
'distance_disk_from_C1'] = centerline.distance_from_C1label # distance between each disk and C1 (or first disk)
xlabel_disks = [
centerline.convert_vertlabel2disklabel[label]
for label in properties['distance_disk_from_C1']
]
xtick_disks = [
properties['distance_disk_from_C1'][label]
for label in properties['distance_disk_from_C1']
]
plt.xticks(xtick_disks, xlabel_disks, rotation=30)
else:
axes[-1].set_xlabel('Position along the spinal cord (in mm)')
plt.show()
# Removing temporary folder
os.chdir(curdir)
if remove_temp_files:
sct.rmtree(path_tmp)
return property_list, properties
def pre_processing(fname_target,
fname_sc_seg,
fname_level=None,
fname_manual_gmseg=None,
new_res=0.3,
square_size_size_mm=22.5,
denoising=True,
verbose=1,
rm_tmp=True,
for_model=False):
printv('\nPre-process data...', verbose, 'normal')
tmp_dir = sct.tmp_create()
sct.copy(fname_target, tmp_dir)
fname_target = ''.join(extract_fname(fname_target)[1:])
sct.copy(fname_sc_seg, tmp_dir)
fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir)
original_info = {
'orientation': None,
'im_sc_seg_rpi': None,
'interpolated_images': []
}
im_target = Image(fname_target).copy()
im_sc_seg = Image(fname_sc_seg).copy()
# get original orientation
printv(' Reorient...', verbose, 'normal')
original_info['orientation'] = im_target.orientation
# assert images are in the same orientation
assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation"
im_target_rpi = im_target.copy().change_orientation(
'RPI', generate_path=True).save()
im_sc_seg_rpi = im_sc_seg.copy().change_orientation(
'RPI', generate_path=True).save()
original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy(
) # target image in RPI will be used to post-process segmentations
# denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy
if denoising:
printv(' Denoise...', verbose, 'normal')
# crop image before denoising to fasten denoising
nx, ny, nz, nt, px, py, pz, pt = im_target_rpi.dim
size_x, size_y = (square_size_size_mm + 1) / px, (square_size_size_mm +
1) / py
size = int(np.ceil(max(size_x, size_y)))
# create mask
fname_mask = 'mask_pre_crop.nii.gz'
sct_create_mask.main([
'-i', im_target_rpi.absolutepath, '-p',
'centerline,' + im_sc_seg_rpi.absolutepath, '-f', 'box', '-size',
str(size), '-o', fname_mask
])
# crop image
cropper = ImageCropper(im_target_rpi)
cropper.get_bbox_from_mask(Image(fname_mask))
im_target_rpi_crop = cropper.crop()
# crop segmentation
cropper = ImageCropper(im_sc_seg_rpi)
cropper.get_bbox_from_mask(Image(fname_mask))
im_sc_seg_rpi_crop = cropper.crop()
# denoising
from sct_maths import denoise_nlmeans
block_radius = 3
block_radius = int(
im_target_rpi_crop.data.shape[2] /
2) if im_target_rpi_crop.data.shape[2] < (block_radius *
2) else block_radius
patch_radius = block_radius - 1
data_denoised = denoise_nlmeans(im_target_rpi_crop.data,
block_radius=block_radius,
patch_radius=patch_radius)
im_target_rpi_crop.data = data_denoised
im_target_rpi = im_target_rpi_crop
im_sc_seg_rpi = im_sc_seg_rpi_crop
else:
fname_mask = None
# interpolate image to reference square image (resample and square crop centered on SC)
printv(' Interpolate data to the model space...', verbose, 'normal')
list_im_slices = interpolate_im_to_ref(im_target_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm)
original_info[
'interpolated_images'] = list_im_slices # list of images (not Slice() objects)
printv(' Mask data using the spinal cord segmentation...', verbose,
'normal')
list_sc_seg_slices = interpolate_im_to_ref(
im_sc_seg_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm,
interpolation_mode=1)
for i in range(len(list_im_slices)):
# list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0
list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i],
thr_min=0.5,
thr_max=1)
list_im_slices[
i].data = list_im_slices[i].data * list_sc_seg_slices[i].data
printv(' Split along rostro-caudal direction...', verbose, 'normal')
list_slices_target = [
Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[])
for i, im_slice in enumerate(list_im_slices)
]
# load vertebral levels
if fname_level is not None:
printv(' Load vertebral levels...', verbose, 'normal')
# copy level file to tmp dir
os.chdir(curdir)
sct.copy(fname_level, tmp_dir)
os.chdir(tmp_dir)
# change fname level to only file name (path = tmp dir now)
fname_level = ''.join(extract_fname(fname_level)[1:])
# load levels
list_slices_target = load_level(list_slices_target, fname_level)
os.chdir(curdir)
# load manual gmseg if there is one (model data)
if fname_manual_gmseg is not None:
printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal')
list_slices_target = load_manual_gmseg(list_slices_target,
fname_manual_gmseg,
tmp_dir,
im_sc_seg_rpi,
new_res,
square_size_size_mm,
for_model=for_model,
fname_mask=fname_mask)
if rm_tmp:
# remove tmp folder
sct.rmtree(tmp_dir)
return list_slices_target, original_info
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialize parameters
param = Param()
# Initialization
fname_output = ''
path_out = ''
fname_src_seg = ''
fname_dest_seg = ''
fname_src_label = ''
fname_dest_label = ''
generate_warpinv = 1
start_time = time.time()
# get default registration parameters
# step1 = Paramreg(step='1', type='im', algo='syn', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5')
step0 = Paramreg(step='0', type='im', algo='syn', metric='MI', iter='0', shrink='1', smooth='0', gradStep='0.5',
slicewise='0', dof='Tx_Ty_Tz_Rx_Ry_Rz') # only used to put src into dest space
step1 = Paramreg(step='1', type='im')
paramreg = ParamregMultiStep([step0, step1])
parser = get_parser(paramreg=paramreg)
arguments = parser.parse(args)
# get arguments
fname_src = arguments['-i']
fname_dest = arguments['-d']
if '-iseg' in arguments:
fname_src_seg = arguments['-iseg']
if '-dseg' in arguments:
fname_dest_seg = arguments['-dseg']
if '-ilabel' in arguments:
fname_src_label = arguments['-ilabel']
if '-dlabel' in arguments:
fname_dest_label = arguments['-dlabel']
if '-o' in arguments:
fname_output = arguments['-o']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-owarp' in arguments:
fname_output_warp = arguments['-owarp']
else:
fname_output_warp = ''
if '-initwarp' in arguments:
fname_initwarp = os.path.abspath(arguments['-initwarp'])
else:
fname_initwarp = ''
if '-initwarpinv' in arguments:
fname_initwarpinv = os.path.abspath(arguments['-initwarpinv'])
else:
fname_initwarpinv = ''
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
padding = arguments['-z']
if "-param" in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
path_qc = arguments.get("-qc", None)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
identity = int(arguments['-identity'])
interp = arguments['-x']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# sct.printv(arguments)
sct.printv('\nInput parameters:')
sct.printv(' Source .............. ' + fname_src)
sct.printv(' Destination ......... ' + fname_dest)
sct.printv(' Init transfo ........ ' + fname_initwarp)
sct.printv(' Mask ................ ' + fname_mask)
sct.printv(' Output name ......... ' + fname_output)
# sct.printv(' Algorithm ........... '+paramreg.algo)
# sct.printv(' Number of iterations '+paramreg.iter)
# sct.printv(' Metric .............. '+paramreg.metric)
sct.printv(' Remove temp files ... ' + str(remove_temp_files))
sct.printv(' Verbose ............. ' + str(verbose))
# update param
param.verbose = verbose
param.padding = padding
param.fname_mask = fname_mask
param.remove_temp_files = remove_temp_files
# Get if input is 3D
sct.printv('\nCheck if input data are 3D...', verbose)
sct.check_if_3d(fname_src)
sct.check_if_3d(fname_dest)
# Check if user selected type=seg, but did not input segmentation data
if 'paramreg_user' in locals():
if True in ['type=seg' in paramreg_user[i] for i in range(len(paramreg_user))]:
if fname_src_seg == '' or fname_dest_seg == '':
sct.printv('\nERROR: if you select type=seg you must specify -iseg and -dseg flags.\n', 1, 'error')
# 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)
# check if source and destination images have the same name (related to issue #373)
# If so, change names to avoid conflict of result files and warns the user
suffix_src, suffix_dest = '_reg', '_reg'
if file_src == file_dest:
suffix_src, suffix_dest = '_src_reg', '_dest_reg'
# define output folder and file name
if fname_output == '':
path_out = '' if not path_out else path_out # output in user's current directory
file_out = file_src + suffix_src
file_out_inv = file_dest + suffix_dest
ext_out = ext_src
else:
path, file_out, ext_out = sct.extract_fname(fname_output)
path_out = path if not path_out else path_out
file_out_inv = file_out + '_inv'
# create temporary folder
path_tmp = sct.tmp_create()
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_src).save(os.path.join(path_tmp, "src.nii"))
Image(fname_dest).save(os.path.join(path_tmp, "dest.nii"))
if fname_src_seg:
Image(fname_src_seg).save(os.path.join(path_tmp, "src_seg.nii"))
if fname_dest_seg:
Image(fname_dest_seg).save(os.path.join(path_tmp, "dest_seg.nii"))
if fname_src_label:
Image(fname_src_label).save(os.path.join(path_tmp, "src_label.nii"))
Image(fname_dest_label).save(os.path.join(path_tmp, "dest_label.nii"))
if fname_mask != '':
Image(fname_mask).save(os.path.join(path_tmp, "mask.nii.gz"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# reorient destination to RPI
Image('dest.nii').change_orientation("RPI").save('dest_RPI.nii')
if fname_dest_seg:
Image('dest_seg.nii').change_orientation("RPI").save('dest_seg_RPI.nii')
if fname_dest_label:
Image('dest_label.nii').change_orientation("RPI").save('dest_label_RPI.nii')
if identity:
# overwrite paramreg and only do one identity transformation
step0 = Paramreg(step='0', type='im', algo='syn', metric='MI', iter='0', shrink='1', smooth='0', gradStep='0.5')
paramreg = ParamregMultiStep([step0])
# Put source into destination space using header (no estimation -- purely based on header)
# TODO: Check if necessary to do that
# TODO: use that as step=0
# sct.printv('\nPut source into destination space using header...', verbose)
# sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[dest_pad.nii,src.nii,1,16] -c 0 -f 1 -s 0 -o
# [regAffine,src_regAffine.nii] -n BSpline[3]', verbose)
# if segmentation, also do it for seg
# initialize list of warping fields
warp_forward = []
warp_inverse = []
# initial warping is specified, update list of warping fields and skip step=0
if fname_initwarp:
sct.printv('\nSkip step=0 and replace with initial transformations: ', param.verbose)
sct.printv(' ' + fname_initwarp, param.verbose)
# sct.copy(fname_initwarp, 'warp_forward_0.nii.gz')
warp_forward = [fname_initwarp]
start_step = 1
if fname_initwarpinv:
warp_inverse = [fname_initwarpinv]
else:
sct.printv('\nWARNING: No initial inverse warping field was specified, therefore the inverse warping field '
'will NOT be generated.', param.verbose, 'warning')
generate_warpinv = 0
else:
start_step = 0
# loop across registration steps
for i_step in range(start_step, len(paramreg.steps)):
sct.printv('\n--\nESTIMATE TRANSFORMATION FOR STEP #' + str(i_step), param.verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = 'src.nii'
dest = 'dest_RPI.nii'
interp_step = 'spline'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'src_seg.nii'
dest = 'dest_seg_RPI.nii'
interp_step = 'nn'
elif paramreg.steps[str(i_step)].type == 'label':
src = 'src_label.nii'
dest = 'dest_label_RPI.nii'
interp_step = 'nn'
else:
# src = dest = interp_step = None
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>0, apply warp_forward_concat to the src image to be used
if i_step > 0:
sct.printv('\nApply transformation from previous step', param.verbose)
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o',
sct.add_suffix(src, '_reg'), '-x', interp_step], verbose)
src = sct.add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations
sct.printv('\nConcatenate transformations...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_forward), '-d', 'dest.nii', '-o', 'warp_src2dest.nii.gz'],
verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse), '-d', 'src.nii', '-o', 'warp_dest2src.nii.gz'],
verbose)
# Apply warping field to src data
sct.printv('\nApply transfo source --> dest...', verbose)
sct.run(['sct_apply_transfo', '-i', 'src.nii', '-o', 'src_reg.nii', '-d', 'dest.nii', '-w', 'warp_src2dest.nii.gz',
'-x', interp], verbose)
sct.printv('\nApply transfo dest --> source...', verbose)
sct.run(['sct_apply_transfo', '-i', 'dest.nii', '-o', 'dest_reg.nii', '-d', 'src.nii', '-w', 'warp_dest2src.nii.gz',
'-x', interp], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
# generate: src_reg
fname_src2dest = sct.generate_output_file(os.path.join(path_tmp, "src_reg.nii"),
os.path.join(path_out, file_out + ext_out), verbose)
# generate: forward warping field
if fname_output_warp == '':
fname_output_warp = os.path.join(path_out, 'warp_' + file_src + '2' + file_dest + '.nii.gz')
sct.generate_output_file(os.path.join(path_tmp, "warp_src2dest.nii.gz"), fname_output_warp, verbose)
if generate_warpinv:
# generate: dest_reg
fname_dest2src = sct.generate_output_file(os.path.join(path_tmp, "dest_reg.nii"),
os.path.join(path_out, file_out_inv + ext_dest), verbose)
# generate: inverse warping field
sct.generate_output_file(os.path.join(path_tmp, "warp_dest2src.nii.gz"),
os.path.join(path_out, 'warp_' + file_dest + '2' + file_src + '.nii.gz'), verbose)
# Delete temporary files
if remove_temp_files:
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)
if path_qc is not None:
if fname_dest_seg:
generate_qc(fname_src2dest, fname_in2=fname_dest, fname_seg=fname_dest_seg, args=args,
path_qc=os.path.abspath(path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_multimodal')
else:
sct.printv('WARNING: Cannot generate QC because it requires destination segmentation.', 1, 'warning')
if generate_warpinv:
sct.display_viewer_syntax([fname_src, fname_dest2src], verbose=verbose)
sct.display_viewer_syntax([fname_dest, fname_src2dest], 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
algo_fitting = self.algo_fitting
# 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
sct.run(['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, algo_fitting, self.degree, 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, algo_fitting, self.degree, 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))
sct.run(
['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 tqdm(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 tqdm(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...')
sct.run(['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...')
sct.run(['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 main(args=None):
if args is None:
args = sys.argv[1:]
# get parser
parser = get_parser()
arguments = parser.parse(args)
# set param arguments ad inputted by user
fname_mask = arguments["-m"]
# SC segmentation
if '-s' in arguments:
fname_sc = arguments["-s"]
if not os.path.isfile(fname_sc):
fname_sc = None
printv('WARNING: -s input file: "' + arguments['-s'] + '" does not exist.\n', 1, 'warning')
else:
fname_sc = None
# Reference image
if '-i' in arguments:
fname_ref = arguments["-i"]
if not os.path.isfile(fname_sc):
fname_ref = None
printv('WARNING: -i input file: "' + arguments['-i'] + '" does not exist.\n', 1, 'warning')
else:
fname_ref = None
# Path to template
if '-f' in arguments:
path_template = arguments["-f"]
if not os.path.isdir(path_template) and os.path.exists(path_template):
path_template = None
printv("ERROR output directory %s is not a valid directory" % path_template, 1, 'error')
else:
path_template = None
# Output Folder
if '-ofolder' in arguments:
path_results = arguments["-ofolder"]
if not os.path.isdir(path_results) and os.path.exists(path_results):
printv("ERROR output directory %s is not a valid directory" % path_results, 1, 'error')
if not os.path.exists(path_results):
os.makedirs(path_results)
else:
path_results = './'
# Remove temp folder
if '-r' in arguments:
rm_tmp = bool(int(arguments['-r']))
else:
rm_tmp = True
# Verbosity
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# create the Lesion constructor
lesion_obj = AnalyzeLeion(fname_mask=fname_mask,
fname_sc=fname_sc,
fname_ref=fname_ref,
path_template=path_template,
path_ofolder=path_results,
verbose=verbose)
# run the analyze
lesion_obj.analyze()
# remove tmp_dir
if rm_tmp:
sct.rmtree(lesion_obj.tmp_dir)
printv('\nDone! To view the labeled lesion file (one value per lesion), type:', verbose)
if fname_ref is not None:
printv('fslview ' + fname_mask + ' ' + os.path.join(path_results, lesion_obj.fname_label) + ' -l Red-Yellow -t 0.7 & \n', verbose, 'info')
else:
printv('fslview ' + os.path.join(path_results, lesion_obj.fname_label) + ' -l Red-Yellow -t 0.7 & \n', verbose, 'info')
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param.straighten_fitting = arguments['-straighten-fitting']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct.run(['sct_apply_transfo', '-i', ftmp_seg, '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.algo_fitting = param.straighten_fitting
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
s, o = sct.run(['sct_concat_transfo', '-w', 'warp_straight2curve.nii.gz', '-d', ftmp_data, '-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run(['sct_apply_transfo', '-i', ftmp_label, '-o', add_suffix(ftmp_label, '_straight'), '-d', add_suffix(ftmp_seg, '_straight'), '-w', 'warp_curve2straight.nii.gz', '-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run(['sct_concat_transfo', '-w', 'warp_curve2straight.nii.gz,straight2templateAffine.txt', '-d', 'template.nii', '-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run(['sct_apply_transfo', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_straightAffine'), '-d', ftmp_template, '-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run(['sct_apply_transfo', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_straightAffine'), '-d', ftmp_template, '-w', 'warp_curve2straightAffine.nii.gz', '-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct.run(['sct_apply_transfo', '-i', src_seg, '-d', dest_seg, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run(['sct_concat_transfo', '-w', 'warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward), '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz'], verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse) + ',warp_straight2curve.nii.gz', '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
else:
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse) + ',-straight2templateAffine.txt,warp_straight2curve.nii.gz', '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_forward), '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse), '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz'], verbose)
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def main(args=None):
# initializations
param = Param()
# check user arguments
if args is None:
args = sys.argv[1:]
# get parser info
parser = get_parser()
arguments = parser.parse_args(args)
param.download = int(arguments.download)
param.path_data = arguments.path
functions_to_test = arguments.function
param.remove_tmp_file = int(arguments.remove_temps)
jobs = arguments.jobs
param.verbose = arguments.verbose
sct.init_sct(log_level=param.verbose, update=True) # Update log level
start_time = time.time()
# get absolute path and add slash at the end
param.path_data = os.path.abspath(param.path_data)
# check existence of testing data folder
if not os.path.isdir(param.path_data) or param.download:
downloaddata(param)
# display path to data
sct.printv('\nPath to testing data: ' + param.path_data, param.verbose)
# create temp folder that will have all results and go in it
path_tmp = os.path.abspath(arguments.execution_folder or sct.tmp_create(verbose=param.verbose))
curdir = os.getcwd()
os.chdir(path_tmp)
functions_parallel = list()
functions_serial = list()
if functions_to_test:
for f in functions_to_test:
if f in get_functions_parallelizable():
functions_parallel.append(f)
elif f in get_functions_nonparallelizable():
functions_serial.append(f)
else:
sct.printv('Command-line usage error: Function "%s" is not part of the list of testing functions' % f, type='error')
jobs = min(jobs, len(functions_parallel))
else:
functions_parallel = get_functions_parallelizable()
functions_serial = get_functions_nonparallelizable()
if arguments.continue_from:
first_func = arguments.continue_from
if first_func in functions_parallel:
functions_serial = []
functions_parallel = functions_parallel[functions_parallel.index(first_func):]
elif first_func in functions_serial:
functions_serial = functions_serial[functions_serial.index(first_func):]
if arguments.check_filesystem and jobs != 1:
print("Check filesystem used -> jobs forced to 1")
jobs = 1
print("Will run through the following tests:")
if functions_serial:
print("- sequentially: {}".format(" ".join(functions_serial)))
if functions_parallel:
print("- in parallel with {} jobs: {}".format(jobs, " ".join(functions_parallel)))
list_status = []
for name, functions in (
("serial", functions_serial),
("parallel", functions_parallel),
):
if not functions:
continue
if any([s for (f, s) in list_status]) and arguments.abort_on_failure:
break
try:
if functions == functions_parallel and jobs != 1:
pool = multiprocessing.Pool(processes=jobs)
results = list()
# loop across functions and run tests
for f in functions:
func_param = copy.deepcopy(param)
func_param.path_output = f
res = pool.apply_async(process_function_multiproc, (f, func_param,))
results.append(res)
else:
pool = None
for idx_function, f in enumerate(functions):
print_line('Checking ' + f)
if functions == functions_serial or jobs == 1:
if arguments.check_filesystem:
if os.path.exists(os.path.join(path_tmp, f)):
shutil.rmtree(os.path.join(path_tmp, f))
sig_0 = fs_signature(path_tmp)
func_param = copy.deepcopy(param)
func_param.path_output = f
res = process_function(f, func_param)
if arguments.check_filesystem:
sig_1 = fs_signature(path_tmp)
fs_ok(sig_0, sig_1, exclude=(f,))
else:
res = results[idx_function].get()
list_output, list_status_function = res
# manage status
if any(list_status_function):
if 1 in list_status_function:
print_fail()
status = (f, 1)
else:
print_warning()
status = (f, 99)
for output in list_output:
for line in output.splitlines():
print(" %s" % line)
else:
print_ok()
if param.verbose:
for output in list_output:
for line in output.splitlines():
print(" %s" % line)
status = (f, 0)
# append status function to global list of status
list_status.append(status)
if any([s for (f, s) in list_status]) and arguments.abort_on_failure:
break
except KeyboardInterrupt:
raise
finally:
if pool:
pool.terminate()
pool.join()
print('status: ' + str([s for (f, s) in list_status]))
if any([s for (f, s) in list_status]):
print("Failures: {}".format(" ".join([f for (f, s) in list_status if s])))
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('Finished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's\n')
# come back
os.chdir(curdir)
# remove temp files
if param.remove_tmp_file and arguments.execution_folder is None:
sct.printv('\nRemove temporary files...', 0)
sct.rmtree(path_tmp)
e = 0
if any([s for (f, s) in list_status]):
e = 1
# print(e)
sys.exit(e)
def create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data + ext_data)
path_tmp = sct.tmp_create(basename="create_mask", verbose=param.verbose)
sct.printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
sct.printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv('\nDimensions:', param.verbose)
sct.printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in ' + os.path.basename(__file__) + ': Input image is 4d but output mask will be 3D from first time slice.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = msct_image.empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# get file name
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(['sct_label_utils', '-i', 'point_RPI.nii', '-display'], verbose=param.verbose)
# parse to get coordinate
# TODO fixup... this is quite magic
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(np.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param, center, param.shape, param.size, im_data=im_data)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(path_tmp)
sct.display_viewer_syntax([param.fname_data, param.fname_out], colormaps=['gray', 'red'], opacities=['', '0.5'])
def check_and_correct_segmentation(fname_segmentation, fname_centerline, folder_output='', threshold_distance=5.0,
remove_temp_files=1, verbose=0):
"""
This function takes the outputs of isct_propseg (centerline and segmentation) and check if the centerline of the
segmentation is coherent with the centerline provided by the isct_propseg, especially on the edges (related
to issue #1074).
Args:
fname_segmentation: filename of binary segmentation
fname_centerline: filename of binary centerline
threshold_distance: threshold, in mm, beyond which centerlines are not coherent
verbose:
Returns: None
"""
sct.printv('\nCheck consistency of segmentation...', verbose)
# creating a temporary folder in which all temporary files will be placed and deleted afterwards
path_tmp = sct.tmp_create(basename="propseg", verbose=verbose)
from sct_convert import convert
convert(fname_segmentation, os.path.join(path_tmp, "tmp.segmentation.nii.gz"), verbose=0)
convert(fname_centerline, os.path.join(path_tmp, "tmp.centerline.nii.gz"), verbose=0)
fname_seg_absolute = os.path.abspath(fname_segmentation)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert segmentation image to RPI
im_input = Image('tmp.segmentation.nii.gz')
image_input_orientation = im_input.orientation
sct_image.main("-i tmp.segmentation.nii.gz -setorient RPI -o tmp.segmentation_RPI.nii.gz -v 0".split())
sct_image.main("-i tmp.centerline.nii.gz -setorient RPI -o tmp.centerline_RPI.nii.gz -v 0".split())
# go through segmentation image, and compare with centerline from propseg
im_seg = Image('tmp.segmentation_RPI.nii.gz')
im_centerline = Image('tmp.centerline_RPI.nii.gz')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
# extraction of centerline provided by isct_propseg and computation of center of mass for each slice
# the centerline is defined as the center of the tubular mesh outputed by propseg.
centerline, key_centerline = {}, []
for i in range(nz):
slice = im_centerline.data[:, :, i]
if np.any(slice):
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
centerline[str(i)] = [x_centerline, y_centerline]
key_centerline.append(i)
minz_centerline = np.min(key_centerline)
maxz_centerline = np.max(key_centerline)
mid_slice = int((maxz_centerline - minz_centerline) / 2)
# for each slice of the segmentation, check if only one object is present. If not, remove the slice from segmentation.
# If only one object (the spinal cord) is present in the slice, check if its center of mass is close to the centerline of isct_propseg.
slices_to_remove = [False] * nz # flag that decides if the slice must be removed
for i in range(minz_centerline, maxz_centerline + 1):
# extraction of slice
slice = im_seg.data[:, :, i]
distance = -1
label_objects, nb_labels = ndi.label(slice) # count binary objects in the slice
if nb_labels > 1: # if there is more that one object in the slice, the slice is removed from the segmentation
slices_to_remove[i] = True
elif nb_labels == 1: # check if the centerline is coherent with the one from isct_propseg
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
slice_nearest_coord = min(key_centerline, key=lambda x: abs(x - i))
coord_nearest_coord = centerline[str(slice_nearest_coord)]
distance = np.sqrt(((x_centerline - coord_nearest_coord[0]) * px) ** 2 +
((y_centerline - coord_nearest_coord[1]) * py) ** 2 +
((i - slice_nearest_coord) * pz) ** 2)
if distance >= threshold_distance: # threshold must be adjusted, default is 5 mm
slices_to_remove[i] = True
# Check list of removal and keep one continuous centerline (improve this comment)
# Method:
# starting from mid-centerline (in both directions), the first True encountered is applied to all following slices
slice_to_change = False
for i in range(mid_slice, nz):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
slice_to_change = False
for i in range(mid_slice, 0, -1):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
for i in range(0, nz):
# remove the slice
if slices_to_remove[i]:
im_seg.data[:, :, i] *= 0
# saving the image
im_seg.save('tmp.segmentation_RPI_c.nii.gz')
# replacing old segmentation with the corrected one
sct_image.main('-i tmp.segmentation_RPI_c.nii.gz -setorient {} -o {} -v 0'.
format(image_input_orientation, fname_seg_absolute).split())
os.chdir(curdir)
# display information about how much of the segmentation has been corrected
# remove temporary files
if remove_temp_files:
# sct.printv("\nRemove temporary files...", verbose)
sct.rmtree(path_tmp)
def main(args=None):
if args is None:
args = sys.argv[1:]
# create param objects
param_seg = ParamSeg()
param_data = ParamData()
param_model = ParamModel()
param = Param()
# get parser
parser = get_parser()
arguments = parser.parse(args)
# set param arguments ad inputted by user
param_seg.fname_im = arguments["-i"]
param_seg.fname_im_original = arguments["-i"]
param_seg.fname_seg = arguments["-s"]
if '-vertfile' in arguments:
if extract_fname(arguments['-vertfile'])[1].lower() == "none":
param_seg.fname_level = None
elif os.path.isfile(arguments['-vertfile']):
param_seg.fname_level = arguments['-vertfile']
else:
param_seg.fname_level = None
printv('WARNING: -vertfile input file: "' + arguments['-vertfile'] + '" does not exist.\nSegmenting GM without using vertebral information', 1, 'warning')
if '-denoising' in arguments:
param_data.denoising = bool(int(arguments['-denoising']))
if '-normalization' in arguments:
param_data.normalization = bool(int(arguments['-normalization']))
if '-p' in arguments:
param_data.register_param = arguments['-p']
if '-w-levels' in arguments:
param_seg.weight_level = arguments['-w-levels']
if '-w-coordi' in arguments:
param_seg.weight_coord = arguments['-w-coordi']
if '-thr-sim' in arguments:
param_seg.thr_similarity = arguments['-thr-sim']
if '-model' in arguments:
param_model.path_model_to_load = os.path.abspath(arguments['-model'])
if '-res-type' in arguments:
param_seg.type_seg = arguments['-res-type']
if '-ref' in arguments:
param_seg.fname_manual_gmseg = arguments['-ref']
if '-ofolder' in arguments:
param_seg.path_results = os.path.abspath(arguments['-ofolder'])
param_seg.qc = arguments.get("-qc", None)
if '-r' in arguments:
param.rm_tmp = bool(int(arguments['-r']))
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
start_time = time.time()
seg_gm = SegmentGM(param_seg=param_seg, param_data=param_data, param_model=param_model, param=param)
seg_gm.segment()
elapsed_time = time.time() - start_time
printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', param.verbose)
# save quality control and sct.printv(info)
if param_seg.type_seg == 'bin':
wm_col = 'red'
gm_col = 'blue'
b = '0,1'
else:
wm_col = 'blue-lightblue'
gm_col = 'red-yellow'
b = '0.4,1'
if param_seg.qc is not None:
generate_qc(param_seg.fname_im_original, seg_gm.fname_res_gmseg,
seg_gm.fname_res_wmseg, param_seg, args, os.path.abspath(param_seg.qc))
if param.rm_tmp:
# remove tmp_dir
sct.rmtree(seg_gm.tmp_dir)
sct.display_viewer_syntax([param_seg.fname_im_original, seg_gm.fname_res_gmseg, seg_gm.fname_res_wmseg], colormaps=['gray', gm_col, wm_col], minmax=['', b, b], opacities=['1', '0.7', '0.7'], verbose=param.verbose)
if '-bmax' in arguments and arguments['-bmax'] == '1':
cmd += ['-bmax']
if '-bzmax' in arguments and arguments['-bzmax'] == '1':
cmd += ['-bzmax']
if '-o' in arguments:
path_output, fname_output, ext = sct.extract_fname(arguments['-o'])
cmd += ['-o', fname_output + ext]
if '-r' in arguments:
rm_tmp = bool(int(arguments['-r']))
# # Computation of Dice coefficient using Python implementation.
# # commented for now as it does not cover all the feature of isct_dice_coefficient
# #from spinalcordtoolbox.image import Image, compute_dice
# #dice = compute_dice(Image(fname_input1), Image(fname_input2), mode='3d', zboundaries=False)
# #sct.printv('Dice (python-based) = ' + str(dice), verbose)
status, output = sct.run(cmd, verbose, is_sct_binary=True)
os.chdir(curdir) # go back to original directory
# copy output file into original directory
if '-o' in arguments:
sct.copy(os.path.join(tmp_dir, fname_output+ext), os.path.join(path_output, fname_output+ext))
# remove tmp_dir
if rm_tmp:
sct.rmtree(tmp_dir)
sct.printv(output, verbose)
def main(args=None):
# initialization
start_time = time.time()
path_out = '.'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
param.fname_bvecs = arguments['-bvec']
if '-bval' in arguments:
param.fname_bvals = arguments['-bval']
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-thr' in arguments:
param.otsu = arguments['-thr']
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
param.fname_bvecs = os.path.abspath(param.fname_bvecs)
if param.fname_bvals != '':
param.fname_bvals = os.path.abspath(param.fname_bvals)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_mask, file_mask, ext_mask = sct.extract_fname(param.fname_mask)
path_tmp = sct.tmp_create(basename="dmri_moco", verbose=param.verbose)
# names of files in temporary folder
mask_name = 'mask'
bvecs_fname = 'bvecs.txt'
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, os.path.join(path_tmp, "dmri.nii"))
sct.copy(param.fname_bvecs, os.path.join(path_tmp, bvecs_fname), verbose=param.verbose)
if param.fname_mask != '':
sct.copy(param.fname_mask, os.path.join(path_tmp, mask_name + ext_mask), verbose=param.verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# update field in param (because used later).
# TODO: make this cleaner...
if param.fname_mask != '':
param.fname_mask = mask_name + ext_mask
# run moco
fname_data_moco_tmp = dmri_moco(param)
# generate b0_moco_mean and dwi_moco_mean
args = ['-i', fname_data_moco_tmp, '-bvec', 'bvecs.txt', '-a', '1', '-v', '0']
if not param.fname_bvals == '':
# if bvals file is provided
args += ['-bval', param.fname_bvals]
fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean = sct_dmri_separate_b0_and_dwi.main(args=args)
# come back
os.chdir(curdir)
# Generate output files
fname_dmri_moco = os.path.join(path_out, file_data + param.suffix + ext_data)
fname_dmri_moco_b0_mean = sct.add_suffix(fname_dmri_moco, '_b0_mean')
fname_dmri_moco_dwi_mean = sct.add_suffix(fname_dmri_moco, '_dwi_mean')
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(fname_data_moco_tmp, fname_dmri_moco, param.verbose)
sct.generate_output_file(fname_b0_mean, fname_dmri_moco_b0_mean, param.verbose)
sct.generate_output_file(fname_dwi_mean, fname_dmri_moco_dwi_mean, param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', param.verbose)
sct.display_viewer_syntax([fname_dmri_moco, file_data], mode='ortho,ortho')
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param_centerline = ParamCenterline(
algo_fitting=arguments['-centerline-algo'],
smooth=arguments['-centerline-smooth'])
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg, param_centerline)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-w', 'warp_curve2straight.nii.gz',
'-d', 'straight_ref.nii.gz',
'-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.param_centerline = param_centerline
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct_concat_transfo.main(args=[
'-w', 'warp_straight2curve.nii.gz',
'-d', ftmp_data,
'-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_label,
'-o', add_suffix(ftmp_label, '_straight'),
'-d', add_suffix(ftmp_seg, '_straight'),
'-w', 'warp_curve2straight.nii.gz',
'-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct_concat_transfo.main(args=[
'-w', ['warp_curve2straight.nii.gz', 'straight2templateAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_data,
'-o', add_suffix(ftmp_data, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-o', add_suffix(ftmp_seg, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz',
'-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct_apply_transfo.main(args=[
'-i', src_seg,
'-d', dest_seg,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations: anat --> template
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
warp_forward.insert(0, 'warp_curve2straightAffine.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Concatenate transformations: template --> anat
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
else:
warp_inverse.append('straight2templateAffine.txt')
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['straight2templateAffine.txt'],
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['template2subjectAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def 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
sct.run(['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)
# sct.run('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)
# sct.run('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)
# sct.run(cmd)
#
# # Apply deformation to input image
# sct.printv('\nApply transformation to input image...', verbose)
# sct.run('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)
sct.run(["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)
def register_data(im_src,
im_dest,
param_reg,
path_copy_warp=None,
rm_tmp=True):
'''
Parameters
----------
im_src: class Image: source image
im_dest: class Image: destination image
param_reg: str: registration parameter
path_copy_warp: path: path to copy the warping fields
Returns: im_src_reg: class Image: source image registered on destination image
-------
'''
# im_src and im_dest are already preprocessed (in theory: im_dest = mean_image)
# binarize images to get seg
im_src_seg = binarize(im_src, thr_min=1, thr_max=1)
im_dest_seg = binarize(im_dest)
# create tmp dir and go in it
tmp_dir = sct.tmp_create()
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_src_seg = 'src_seg.nii.gz'
im_src_seg.save(fname_src_seg)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
fname_dest_seg = 'dest_seg.nii.gz'
im_dest_seg.save(fname_dest_seg)
# do registration using param_reg
sct_register_multimodal.main(args=[
'-i', fname_src, '-d', fname_dest, '-iseg', fname_src_seg, '-dseg',
fname_dest_seg, '-param', param_reg
])
# get registration result
fname_src_reg = add_suffix(fname_src, '_reg')
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
# copy warping fields
if path_copy_warp is not None and os.path.isdir(
os.path.abspath(path_copy_warp)):
path_copy_warp = os.path.abspath(path_copy_warp)
file_src = extract_fname(fname_src)[1]
file_dest = extract_fname(fname_dest)[1]
fname_src2dest = 'warp_' + file_src + '2' + file_dest + '.nii.gz'
fname_dest2src = 'warp_' + file_dest + '2' + file_src + '.nii.gz'
sct.copy(os.path.join(tmp_dir, fname_src2dest), path_copy_warp)
sct.copy(os.path.join(tmp_dir, fname_dest2src), path_copy_warp)
if rm_tmp:
# remove tmp dir
sct.rmtree(tmp_dir)
# return res image
return im_src_reg, fname_src2dest, fname_dest2src
def crop_with_gui(self):
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Initialization
fname_data = self.input_filename
suffix_out = '_crop'
remove_temp_files = self.rm_tmp_files
verbose = self.verbose
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
verbose)
# check if 4D data
if not nt == 1:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) +
': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' data ................... ' + fname_data)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
path_tmp = sct.tmp_create() + "/"
# copy files into tmp folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
Image('data.nii').change_orientation("RPI").save('data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title(
'Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.'
)
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv(
'\nERROR: You have to select two points. Exit program.\n', 1,
'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.absolutepath = 'data_rpi_crop.nii'
nii.save()
# come back
os.chdir(curdir)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "data_rpi_crop.nii"),
os.path.join(path_out, file_out + ext_out))
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
sct.display_viewer_syntax(
files=[os.path.join(path_out, file_out + ext_out)])
def main():
# Initialization
fname_data = ''
interp_factor = param.interp_factor
remove_temp_files = param.remove_temp_files
verbose = param.verbose
suffix = param.suffix
smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# get path of the toolbox
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
# Parameters for debug mode
if param.debug:
fname_data = os.path.join(path_sct, 'testing', 'data', 'errsm_23',
't2', 't2_manual_segmentation.nii.gz')
remove_temp_files = 0
param.mask_size = 10
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:v:r:s:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-s'):
smoothing_sigma = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' segmentation ........... ' + fname_data)
sct.printv(' interp factor .......... ' + str(interp_factor))
sct.printv(' smoothing sigma ........ ' + str(smoothing_sigma))
# check existence of input files
sct.printv('\nCheck existence of input files...')
sct.check_file_exist(fname_data, verbose)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_tmp = sct.tmp_create(basename="binary_to_trilinear", verbose=verbose)
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...',
param.verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# upsample data
sct.printv('\nUpsample data...', verbose)
sct.run([
"sct_resample", "-i", "data.nii", "-x", "linear", "-vox",
str(nx * interp_factor) + 'x' + str(ny * interp_factor) + 'x' +
str(nz * interp_factor), "-o", "data_up.nii"
], verbose)
# Smooth along centerline
sct.printv('\nSmooth along centerline...', verbose)
sct.run([
"sct_smooth_spinalcord", "-i", "data_up.nii", "-s", "data_up.nii",
"-smooth",
str(smoothing_sigma), "-r",
str(remove_temp_files), "-v",
str(verbose)
], verbose)
# downsample data
sct.printv('\nDownsample data...', verbose)
sct.run([
"sct_resample", "-i", "data_up_smooth.nii", "-x", "linear", "-vox",
str(nx) + 'x' + str(ny) + 'x' + str(nz), "-o",
"data_up_smooth_down.nii"
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...')
fname_out = sct.generate_output_file(
os.path.join(path_tmp, "data_up_smooth_down.nii"),
'' + file_data + suffix + ext_data)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) +
's')
# to view results
sct.printv('\nTo view results, type:')
sct.printv('fslview ' + file_data + ' ' + file_data + suffix + ' &\n')
def main(args=None):
if args is None:
args = sys.argv[1:]
# Get parser
parser = get_parser()
arguments = parser.parse(args)
# Set param arguments ad inputted by user
fname_in = arguments["-i"]
contrast = arguments["-c"]
# Segmentation or Centerline line
if '-s' in arguments:
fname_seg = arguments['-s']
if not os.path.isfile(fname_seg):
fname_seg = None
sct.printv(
'WARNING: -s input file: "' + arguments['-s'] +
'" does not exist.\nDetecting PMJ without using segmentation information',
1, 'warning')
else:
fname_seg = None
# Output Folder
if '-ofolder' in arguments:
path_results = arguments["-ofolder"]
if not os.path.isdir(path_results) and os.path.exists(path_results):
sct.printv(
"ERROR output directory %s is not a valid directory" %
path_results, 1, 'error')
if not os.path.exists(path_results):
os.makedirs(path_results)
else:
path_results = '.'
path_qc = arguments.get("-qc", None)
# Remove temp folder
rm_tmp = bool(int(arguments.get("-r", 1)))
# Verbosity
verbose = int(arguments.get("-v", 1))
# Initialize DetectPMJ
detector = DetectPMJ(fname_im=fname_in,
contrast=contrast,
fname_seg=fname_seg,
path_out=path_results,
verbose=verbose)
# run the extraction
fname_out, tmp_dir = detector.apply()
# Remove tmp_dir
if rm_tmp:
sct.rmtree(tmp_dir)
# View results
if fname_out is not None:
if path_qc is not None:
generate_qc(fname_in,
fname_seg=fname_out,
args=args,
path_qc=os.path.abspath(path_qc),
process='sct_detect_pmj')
sct.display_viewer_syntax([fname_in, fname_out],
colormaps=['gray', 'red'])
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialization
# note: mirror servers are listed in order of priority
dict_url = {
'sct_example_data': ['https://osf.io/kjcgs/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180525_sct_example_data.zip'],
'sct_testing_data': ['https://osf.io/z8gaj/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180125_sct_testing_data.zip'],
'PAM50': ['https://osf.io/xz7jk/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180410_PAM50.zip'],
'MNI-Poly-AMU': ['https://osf.io/sh6h4/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170310_MNI-Poly-AMU.zip'],
'gm_model': ['https://osf.io/ugscu/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20160922_gm_model.zip'],
'optic_models': ['https://osf.io/g4fwn/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170413_optic_models.zip'],
'pmj_models': ['https://osf.io/4gufr/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170922_pmj_models.zip'],
'binaries_debian': ['https://osf.io/2egh5/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170915_sct_binaries_linux.tar.gz'],
'binaries_centos': ['https://osf.io/qngj2/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170915_sct_binaries_linux_centos6.tar.gz'],
'binaries_osx': ['https://osf.io/hsa5r/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170915_sct_binaries_osx.tar.gz'],
'course_hawaii17': 'https://osf.io/6exht/?action=download',
'course_paris18': ['https://osf.io/9bmn5/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180612_sct_course-paris18.zip'],
'deepseg_gm_models': ['https://osf.io/b9y4x/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180205_deepseg_gm_models.zip'],
'deepseg_sc_models': ['https://osf.io/avf97/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180610_deepseg_sc_models.zip']
}
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
data_name = arguments['-d']
verbose = int(arguments['-v'])
dest_folder = arguments.get('-o', os.path.abspath(os.curdir))
# Download data
url = dict_url[data_name]
tmp_file = download_data(url, verbose)
# Check if folder already exists
sct.printv('\nCheck if folder already exists...', verbose)
if os.path.isdir(data_name):
sct.printv('WARNING: Folder ' + data_name + ' already exists. Removing it...', 1, 'warning')
sct.rmtree(data_name)
# unzip
unzip(tmp_file, dest_folder, verbose)
sct.printv('\nRemove temporary file...', verbose)
os.remove(tmp_file)
sct.printv('Done!\n', verbose)
def main(args=None):
# initialization
start_time = time.time()
param = Param()
# reducing the number of CPU used for moco (see issue #201)
os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "1"
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
param.verbose = int(arguments['-v'])
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' input file ............' + param.fname_data, param.verbose)
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_tmp = sct.tmp_create(basename="fmri_moco", verbose=param.verbose)
# Copying input data to tmp folder and convert to nii
# TODO: no need to do that (takes time for nothing)
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, os.path.join(path_tmp, "fmri.nii"), squeeze_data=False)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# run moco
fmri_moco(param)
# come back
os.chdir(curdir)
# Generate output files
fname_fmri_moco = os.path.join(path_out, file_data + param.suffix + ext_data)
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
if os.path.isfile(os.path.join(path_tmp, "fmri" + param.suffix + '.nii')):
sct.printv(os.path.join(path_tmp, "fmri" + param.suffix + '.nii'))
sct.printv(os.path.join(path_out, file_data + param.suffix + ext_data))
sct.generate_output_file(os.path.join(path_tmp, "fmri" + param.suffix + '.nii'), os.path.join(path_out, file_data + param.suffix + ext_data), param.verbose)
sct.generate_output_file(os.path.join(path_tmp, "fmri" + param.suffix + '_mean.nii'), os.path.join(path_out, file_data + param.suffix + '_mean' + ext_data), param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', param.verbose)
sct.display_viewer_syntax([fname_fmri_moco, file_data], mode='ortho,ortho')
def check_and_correct_segmentation(fname_segmentation,
fname_centerline,
folder_output='',
threshold_distance=5.0,
remove_temp_files=1,
verbose=0):
"""
This function takes the outputs of isct_propseg (centerline and segmentation) and check if the centerline of the
segmentation is coherent with the centerline provided by the isct_propseg, especially on the edges (related
to issue #1074).
Args:
fname_segmentation: filename of binary segmentation
fname_centerline: filename of binary centerline
threshold_distance: threshold, in mm, beyond which centerlines are not coherent
verbose:
Returns: None
"""
sct.printv('\nCheck consistency of segmentation...', verbose)
# creating a temporary folder in which all temporary files will be placed and deleted afterwards
path_tmp = sct.tmp_create(basename="propseg", verbose=verbose)
from sct_convert import convert
convert(fname_segmentation,
os.path.join(path_tmp, "tmp.segmentation.nii.gz"),
squeeze_data=False,
verbose=0)
convert(fname_centerline,
os.path.join(path_tmp, "tmp.centerline.nii.gz"),
squeeze_data=False,
verbose=0)
fname_seg_absolute = os.path.abspath(fname_segmentation)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert segmentation image to RPI
im_input = Image('tmp.segmentation.nii.gz')
image_input_orientation = orientation(im_input, get=True, verbose=False)
sct_image.main(
"-i tmp.segmentation.nii.gz -setorient RPI -o tmp.segmentation_RPI.nii.gz -v 0"
.split())
sct_image.main(
"-i tmp.centerline.nii.gz -setorient RPI -o tmp.centerline_RPI.nii.gz -v 0"
.split())
# go through segmentation image, and compare with centerline from propseg
im_seg = Image('tmp.segmentation_RPI.nii.gz')
im_centerline = Image('tmp.centerline_RPI.nii.gz')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
# extraction of centerline provided by isct_propseg and computation of center of mass for each slice
# the centerline is defined as the center of the tubular mesh outputed by propseg.
centerline, key_centerline = {}, []
for i in range(nz):
slice = im_centerline.data[:, :, i]
if np.any(slice):
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
centerline[str(i)] = [x_centerline, y_centerline]
key_centerline.append(i)
minz_centerline = np.min(key_centerline)
maxz_centerline = np.max(key_centerline)
mid_slice = int((maxz_centerline - minz_centerline) / 2)
# for each slice of the segmentation, check if only one object is present. If not, remove the slice from segmentation.
# If only one object (the spinal cord) is present in the slice, check if its center of mass is close to the centerline of isct_propseg.
slices_to_remove = [
False
] * nz # flag that decides if the slice must be removed
for i in range(minz_centerline, maxz_centerline + 1):
# extraction of slice
slice = im_seg.data[:, :, i]
distance = -1
label_objects, nb_labels = ndi.label(
slice) # count binary objects in the slice
if nb_labels > 1: # if there is more that one object in the slice, the slice is removed from the segmentation
slices_to_remove[i] = True
elif nb_labels == 1: # check if the centerline is coherent with the one from isct_propseg
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
slice_nearest_coord = min(key_centerline, key=lambda x: abs(x - i))
coord_nearest_coord = centerline[str(slice_nearest_coord)]
distance = np.sqrt((
(x_centerline - coord_nearest_coord[0]) * px)**2 + (
(y_centerline - coord_nearest_coord[1]) * py)**2 +
((i - slice_nearest_coord) * pz)**2)
if distance >= threshold_distance: # threshold must be adjusted, default is 5 mm
slices_to_remove[i] = True
# Check list of removal and keep one continuous centerline (improve this comment)
# Method:
# starting from mid-centerline (in both directions), the first True encountered is applied to all following slices
slice_to_change = False
for i in range(mid_slice, nz):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
slice_to_change = False
for i in range(mid_slice, 0, -1):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
for i in range(0, nz):
# remove the slice
if slices_to_remove[i]:
im_seg.data[:, :, i] *= 0
# saving the image
im_seg.setFileName('tmp.segmentation_RPI_c.nii.gz')
im_seg.save()
# replacing old segmentation with the corrected one
sct_image.main(
'-i tmp.segmentation_RPI_c.nii.gz -setorient {} -o {} -v 0'.format(
image_input_orientation, fname_seg_absolute).split())
os.chdir(curdir)
# display information about how much of the segmentation has been corrected
# remove temporary files
if remove_temp_files:
# sct.printv("\nRemove temporary files...", verbose)
sct.rmtree(path_tmp)
def validation(self):
tmp_dir_val = sct.tmp_create(basename="segment_graymatter_validation")
# copy data into tmp dir val
sct.copy(self.param_seg.fname_manual_gmseg, tmp_dir_val)
sct.copy(self.param_seg.fname_seg, tmp_dir_val)
curdir = os.getcwd()
os.chdir(tmp_dir_val)
fname_manual_gmseg = os.path.basename(
self.param_seg.fname_manual_gmseg)
fname_seg = os.path.basename(self.param_seg.fname_seg)
im_gmseg = self.im_res_gmseg.copy()
im_wmseg = self.im_res_wmseg.copy()
if self.param_seg.type_seg == 'prob':
im_gmseg = binarize(im_gmseg, thr_max=0.5, thr_min=0.5)
im_wmseg = binarize(im_wmseg, thr_max=0.5, thr_min=0.5)
fname_gmseg = 'res_gmseg.nii.gz'
im_gmseg.save(fname_gmseg)
fname_wmseg = 'res_wmseg.nii.gz'
im_wmseg.save(fname_wmseg)
# get manual WM seg:
fname_manual_wmseg = 'manual_wmseg.nii.gz'
sct_maths.main(args=[
'-i', fname_seg, '-sub', fname_manual_gmseg, '-o',
fname_manual_wmseg
])
# compute DC:
try:
status_gm, output_gm = run('sct_dice_coefficient -i ' +
fname_manual_gmseg + ' -d ' +
fname_gmseg + ' -2d-slices 2')
status_wm, output_wm = run('sct_dice_coefficient -i ' +
fname_manual_wmseg + ' -d ' +
fname_wmseg + ' -2d-slices 2')
except Exception:
# put ref and res in the same space if needed
fname_manual_gmseg_corrected = add_suffix(fname_manual_gmseg,
'_reg')
sct_register_multimodal.main(args=[
'-i', fname_manual_gmseg, '-d', fname_gmseg, '-identity', '1'
])
sct_maths.main(args=[
'-i', fname_manual_gmseg_corrected, '-bin', '0.1', '-o',
fname_manual_gmseg_corrected
])
#
fname_manual_wmseg_corrected = add_suffix(fname_manual_wmseg,
'_reg')
sct_register_multimodal.main(args=[
'-i', fname_manual_wmseg, '-d', fname_wmseg, '-identity', '1'
])
sct_maths.main(args=[
'-i', fname_manual_wmseg_corrected, '-bin', '0.1', '-o',
fname_manual_wmseg_corrected
])
# recompute DC
status_gm, output_gm = run('sct_dice_coefficient -i ' +
fname_manual_gmseg_corrected + ' -d ' +
fname_gmseg + ' -2d-slices 2')
status_wm, output_wm = run('sct_dice_coefficient -i ' +
fname_manual_wmseg_corrected + ' -d ' +
fname_wmseg + ' -2d-slices 2')
# save results to a text file
fname_dc = 'dice_coefficient_' + extract_fname(
self.param_seg.fname_im)[1] + '.txt'
file_dc = open(fname_dc, 'w')
if self.param_seg.type_seg == 'prob':
file_dc.write(
'WARNING : the probabilistic segmentations were binarized with a threshold at 0.5 to compute the dice coefficient \n'
)
file_dc.write(
'\n--------------------------------------------------------------\nDice coefficient on the Gray Matter segmentation:\n'
)
file_dc.write(output_gm)
file_dc.write(
'\n\n--------------------------------------------------------------\nDice coefficient on the White Matter segmentation:\n'
)
file_dc.write(output_wm)
file_dc.close()
# compute HD and MD:
fname_hd = 'hausdorff_dist_' + extract_fname(
self.param_seg.fname_im)[1] + '.txt'
run('sct_compute_hausdorff_distance -i ' + fname_gmseg + ' -d ' +
fname_manual_gmseg + ' -thinning 1 -o ' + fname_hd + ' -v ' +
str(self.param.verbose))
# get out of tmp dir to copy results to output folder
os.chdir(curdir)
sct.copy(os.path.join(self.tmp_dir, tmp_dir_val, fname_dc),
self.param_seg.path_results)
sct.copy(os.path.join(self.tmp_dir, tmp_dir_val, fname_hd),
self.param_seg.path_results)
if self.param.rm_tmp:
sct.rmtree(tmp_dir_val)
def main(args=None):
if not args:
args = sys.argv[1:]
# initialize parameters
param = Param()
# call main function
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_bvecs = arguments['-bvec']
average = arguments['-a']
verbose = int(arguments['-v'])
remove_temp_files = int(arguments['-r'])
path_out = arguments['-ofolder']
if '-bval' in arguments:
fname_bvals = arguments['-bval']
else:
fname_bvals = ''
if '-bvalmin' in arguments:
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)
sct.run([
'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)
sct.run([
'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)
sct.generate_output_file(os.path.join(path_tmp, dwi_name + ext), fname_dwi,
verbose)
if average:
sct.generate_output_file(os.path.join(path_tmp, b0_mean_name + ext),
fname_b0_mean, verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_mean_name + ext),
fname_dwi_mean, 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 create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data +
ext_data)
path_tmp = sct.tmp_create(basename="create_mask", verbose=param.verbose)
sct.printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
sct.printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(
os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv('\nDimensions:', param.verbose)
sct.printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv(
'WARNING in ' + os.path.basename(__file__) +
': Input image is 4d but output mask will be 3D from first time slice.',
param.verbose, 'warning')
# extract first volume to have 3d reference
nii = msct_image.empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# get file name
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(
['sct_label_utils', '-i', 'point_RPI.nii', '-display'],
verbose=param.verbose)
# parse to get coordinate
# TODO fixup... this is quite magic
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
np.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param,
center,
param.shape,
param.size,
im_data=im_data)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(path_tmp)
sct.display_viewer_syntax([param.fname_data, param.fname_out],
colormaps=['gray', 'red'],
opacities=['', '0.5'])
def main(args=None):
# initializations
param = Param()
# check user arguments
if args is None:
args = sys.argv[1:]
# get parser info
parser = get_parser()
arguments = parser.parse_args(args)
param.download = int(arguments.download)
param.path_data = arguments.path
functions_to_test = arguments.function
param.remove_tmp_file = int(arguments.remove_temps)
jobs = arguments.jobs
param.verbose = arguments.verbose
start_time = time.time()
# get absolute path and add slash at the end
param.path_data = os.path.abspath(param.path_data)
# check existence of testing data folder
if not os.path.isdir(param.path_data) or param.download:
downloaddata(param)
# display path to data
sct.printv('\nPath to testing data: ' + param.path_data, param.verbose)
# create temp folder that will have all results and go in it
path_tmp = os.path.abspath(arguments.execution_folder or sct.tmp_create(verbose=param.verbose))
curdir = os.getcwd()
os.chdir(path_tmp)
functions_parallel = list()
functions_serial = list()
if functions_to_test:
for f in functions_to_test:
if f in get_functions_parallelizable():
functions_parallel.append(f)
elif f in get_functions_nonparallelizable():
functions_serial.append(f)
else:
sct.printv('Command-line usage error: Function "%s" is not part of the list of testing functions' % f, type='error')
jobs = min(jobs, len(functions_parallel))
else:
functions_parallel = get_functions_parallelizable()
functions_serial = get_functions_nonparallelizable()
if arguments.continue_from:
first_func = arguments.continue_from
if first_func in functions_parallel:
functions_serial = []
functions_parallel = functions_parallel[functions_parallel.index(first_func):]
elif first_func in functions_serial:
functions_serial = functions_serial[functions_serial.index(first_func):]
if arguments.check_filesystem and jobs != 1:
print("Check filesystem used -> jobs forced to 1")
jobs = 1
print("Will run through the following tests:")
if functions_serial:
print("- sequentially: {}".format(" ".join(functions_serial)))
if functions_parallel:
print("- in parallel with {} jobs: {}".format(jobs, " ".join(functions_parallel)))
list_status = []
for name, functions in (
("serial", functions_serial),
("parallel", functions_parallel),
):
if not functions:
continue
if any([s for (f, s) in list_status]) and arguments.abort_on_failure:
break
try:
if functions == functions_parallel and jobs != 1:
pool = multiprocessing.Pool(processes=jobs)
results = list()
# loop across functions and run tests
for f in functions:
func_param = copy.deepcopy(param)
func_param.path_output = f
res = pool.apply_async(process_function_multiproc, (f, func_param,))
results.append(res)
else:
pool = None
for idx_function, f in enumerate(functions):
print_line('Checking ' + f)
if functions == functions_serial or jobs == 1:
if arguments.check_filesystem:
if os.path.exists(os.path.join(path_tmp, f)):
shutil.rmtree(os.path.join(path_tmp, f))
sig_0 = fs_signature(path_tmp)
func_param = copy.deepcopy(param)
func_param.path_output = f
res = process_function(f, func_param)
if arguments.check_filesystem:
sig_1 = fs_signature(path_tmp)
fs_ok(sig_0, sig_1, exclude=(f,))
else:
res = results[idx_function].get()
list_output, list_status_function = res
# manage status
if any(list_status_function):
if 1 in list_status_function:
print_fail()
status = (f, 1)
else:
print_warning()
status = (f, 99)
for output in list_output:
for line in output.splitlines():
print(" %s" % line)
else:
print_ok()
if param.verbose:
for output in list_output:
for line in output.splitlines():
print(" %s" % line)
status = (f, 0)
# append status function to global list of status
list_status.append(status)
if any([s for (f, s) in list_status]) and arguments.abort_on_failure:
break
except KeyboardInterrupt:
raise
finally:
if pool:
pool.terminate()
pool.join()
print('status: ' + str([s for (f, s) in list_status]))
if any([s for (f, s) in list_status]):
print("Failures: {}".format(" ".join([f for (f, s) in list_status if s])))
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('Finished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's\n')
# come back
os.chdir(curdir)
# remove temp files
if param.remove_tmp_file and arguments.execution_folder is None:
sct.printv('\nRemove temporary files...', 0)
sct.rmtree(path_tmp)
e = 0
if any([s for (f, s) in list_status]):
e = 1
# print(e)
sys.exit(e)
def register_slicewise(fname_src,
fname_dest,
fname_mask='',
warp_forward_out='step0Warp.nii.gz',
warp_inverse_out='step0InverseWarp.nii.gz',
paramreg=None,
ants_registration_params=None,
path_qc='./',
remove_temp_files=0,
verbose=0):
im_and_seg = (paramreg.algo == 'centermassrot') and (paramreg.rot_method == 'hog') # bool for simplicity
# future contributor wanting to implement a method that use both im and seg will add: and (paramreg.rot_method == 'OTHER_METHOD')
if im_and_seg is True:
fname_src_im = fname_src[0]
fname_dest_im = fname_dest[0]
fname_src_seg = fname_src[1]
fname_dest_seg = fname_dest[1]
del fname_src
del fname_dest # to be sure it is not missused later
# create temporary folder
path_tmp = sct.tmp_create(basename="register", verbose=verbose)
# copy data to temp folder
sct.printv('\nCopy input data to temp folder...', verbose)
if im_and_seg is False:
convert(fname_src, os.path.join(path_tmp, "src.nii"))
convert(fname_dest, os.path.join(path_tmp, "dest.nii"))
else:
convert(fname_src_im, os.path.join(path_tmp, "src_im.nii"))
convert(fname_dest_im, os.path.join(path_tmp, "dest_im.nii"))
convert(fname_src_seg, os.path.join(path_tmp, "src_seg.nii"))
convert(fname_dest_seg, os.path.join(path_tmp, "dest_seg.nii"))
if fname_mask != '':
convert(fname_mask, os.path.join(path_tmp, "mask.nii.gz"))
# go to temporary folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Calculate displacement
if paramreg.algo == 'centermass':
# translation of center of mass between source and destination in voxel space
register2d_centermassrot('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, rot=0, polydeg=int(paramreg.poly), path_qc=path_qc, verbose=verbose)
elif paramreg.algo == 'centermassrot':
if im_and_seg is False:
# translation of center of mass and rotation based on source and destination first eigenvectors from PCA.
register2d_centermassrot('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, rot=1, polydeg=int(paramreg.poly), path_qc=path_qc, verbose=verbose, pca_eigenratio_th=float(paramreg.pca_eigenratio_th))
else:
# translation based of center of mass and rotation based on the symmetry of the image
register2d_centermassrot(['src_im.nii','src_seg.nii'], ['dest_im.nii', 'dest_seg.nii'], fname_warp=warp_forward_out,
fname_warp_inv=warp_inverse_out, rot=2, polydeg=int(paramreg.poly),
path_qc=path_qc, verbose=verbose)
elif paramreg.algo == 'columnwise':
# scaling R-L, then column-wise center of mass alignment and scaling
register2d_columnwise('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, verbose=verbose, path_qc=path_qc, smoothWarpXY=int(paramreg.smoothWarpXY))
else:
# convert SCT flags into ANTs-compatible flags
algo_dic = {'translation': 'Translation', 'rigid': 'Rigid', 'affine': 'Affine', 'syn': 'SyN', 'bsplinesyn': 'BSplineSyN', 'centermass': 'centermass'}
paramreg.algo = algo_dic[paramreg.algo]
# run slicewise registration
register2d('src.nii', 'dest.nii', fname_mask=fname_mask, fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, paramreg=paramreg, ants_registration_params=ants_registration_params, verbose=verbose)
sct.printv('\nMove warping fields...', verbose)
sct.copy(warp_forward_out, curdir)
sct.copy(warp_inverse_out, curdir)
# go back
os.chdir(curdir)
if remove_temp_files:
sct.rmtree(path_tmp, verbose=verbose)
def main(args=None):
if args is None:
args = sys.argv[1:]
# create param objects
param_seg = ParamSeg()
param_data = ParamData()
param_model = ParamModel()
param = Param()
# get parser
parser = get_parser()
arguments = parser.parse(args)
# set param arguments ad inputted by user
param_seg.fname_im = arguments["-i"]
param_seg.fname_im_original = arguments["-i"]
param_seg.fname_seg = arguments["-s"]
if '-vertfile' in arguments:
if extract_fname(arguments['-vertfile'])[1].lower() == "none":
param_seg.fname_level = None
elif os.path.isfile(arguments['-vertfile']):
param_seg.fname_level = arguments['-vertfile']
else:
param_seg.fname_level = None
printv(
'WARNING: -vertfile input file: "' + arguments['-vertfile'] +
'" does not exist.\nSegmenting GM without using vertebral information',
1, 'warning')
if '-denoising' in arguments:
param_data.denoising = bool(int(arguments['-denoising']))
if '-normalization' in arguments:
param_data.normalization = bool(int(arguments['-normalization']))
if '-p' in arguments:
param_data.register_param = arguments['-p']
if '-w-levels' in arguments:
param_seg.weight_level = arguments['-w-levels']
if '-w-coordi' in arguments:
param_seg.weight_coord = arguments['-w-coordi']
if '-thr-sim' in arguments:
param_seg.thr_similarity = arguments['-thr-sim']
if '-model' in arguments:
param_model.path_model_to_load = os.path.abspath(arguments['-model'])
if '-res-type' in arguments:
param_seg.type_seg = arguments['-res-type']
if '-ratio' in arguments:
param_seg.ratio = arguments['-ratio']
if '-ref' in arguments:
param_seg.fname_manual_gmseg = arguments['-ref']
if '-ofolder' in arguments:
param_seg.path_results = os.path.abspath(arguments['-ofolder'])
param_seg.qc = arguments.get("-qc", None)
if '-r' in arguments:
param.rm_tmp = bool(int(arguments['-r']))
if '-v' in arguments:
param.verbose = arguments['-v']
start_time = time.time()
seg_gm = SegmentGM(param_seg=param_seg,
param_data=param_data,
param_model=param_model,
param=param)
seg_gm.segment()
elapsed_time = time.time() - start_time
printv(
'\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's',
param.verbose)
# save quality control and sct.printv(info)
if param_seg.type_seg == 'bin':
wm_col = 'red'
gm_col = 'blue'
b = '0,1'
else:
wm_col = 'blue-lightblue'
gm_col = 'red-yellow'
b = '0.4,1'
if param_seg.qc is not None:
generate_qc(param_seg.fname_im_original, seg_gm.fname_res_gmseg,
seg_gm.fname_res_wmseg, param_seg, args,
os.path.abspath(param_seg.qc))
if param.rm_tmp:
# remove tmp_dir
sct.rmtree(seg_gm.tmp_dir)
sct.display_viewer_syntax([
param_seg.fname_im_original, seg_gm.fname_res_gmseg,
seg_gm.fname_res_wmseg
],
colormaps=['gray', gm_col, wm_col],
minmax=['', b, b],
opacities=['1', '0.7', '0.7'],
verbose=param.verbose)
def main():
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'h:d:p:r:t:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
sys.exit(0)
if opt == '-d':
param.download = int(arg)
if opt == '-p':
param.path_data = arg
if opt == '-t':
if ',' in arg:
param.data = arg.split(',')
else:
param.data = arg
if opt == '-r':
param.remove_tmp_file = int(arg)
print(param.data)
start_time = time.time()
# download data
if param.download:
sct.printv('\nDownloading testing data...', param.verbose)
# remove data folder if exist
if os.path.exists('PropSeg_data'):
sct.printv('WARNING: PropSeg_data already exists. Removing it...',
param.verbose, 'warning')
sct.rmtree('PropSeg_data')
# clone git repos
sct.run('git clone ' + param.url_git)
# update path_data field
param.path_data = 'PropSeg_data'
# get absolute path and add slash at the end
param.path_data = sct.slash_at_the_end(os.path.abspath(param.path_data), 1)
# segment all data in t1 folder
results_t1 = []
sum_old, sum_new = 0, 0
if 't1' in param.data:
for dirname in os.listdir(os.path.join(param.path_data, "t1")):
if dirname not in ['._.DS_Store', '.DS_Store']:
for filename in os.listdir(
os.path.join(param.path_data, "t1", dirname)):
if filename.startswith('t1') and not filename.endswith(
'_seg.nii.gz') and not filename.endswith(
'_detection.nii.gz') and not filename.endswith(
'.vtk'):
print(dirname, filename)
[d_old, d_new], [r_old, r_new] = segmentation(
os.path.join(param.path_data, "t1", dirname,
filename),
os.path.join(param.path_data, "t1", dirname), 't1')
if d_old == 0:
d_old = 'OK'
sum_old = sum_old + 1
else:
d_old = 'Not In'
if d_new == 0:
d_new = 'OK'
sum_new = sum_new + 1
else:
d_new = 'Not In'
results_t1.append([
dirname, d_old, d_new,
round(r_old, 2),
round(r_new, 2)
])
# compute average
results_t1.append([
'average', sum_old, sum_new,
np.mean([line[3] for line in results_t1]),
np.mean([line[4] for line in results_t1])
])
# segment all data in t2 folder
results_t2 = []
sum_old, sum_new = 0, 0
if 't2' in param.data:
for dirname in os.listdir(os.path.join(param.path_data, "t2")):
if dirname not in ['._.DS_Store', '.DS_Store']:
for filename in os.listdir(
os.path.join(param.path_data, "t2", dirname)):
if filename.startswith('t2_') and not filename.endswith(
'_seg.nii.gz') and not filename.endswith(
'_detection.nii.gz') and not filename.endswith(
'.vtk'):
print(dirname, filename)
[d_old, d_new], [r_old, r_new] = segmentation(
os.path.join(param.path_data, "t2", dirname,
filename),
os.path.join(param.path_data, "t2", dirname), 't2')
if d_old == 0:
d_old = 'OK'
sum_old = sum_old + 1
else:
d_old = 'Not In'
if d_new == 0:
d_new = 'OK'
sum_new = sum_new + 1
else:
d_new = 'Not In'
results_t2.append([
dirname, d_old, d_new,
round(r_old, 2),
round(r_new, 2)
])
# compute average
results_t2.append([
'average', sum_old, sum_new,
np.mean([line[3] for line in results_t2]),
np.mean([line[4] for line in results_t2])
])
results_dmri = []
sum_old, sum_new = 0, 0
if 'dmri' in param.data:
for dirname in os.listdir(os.path.join(param.path_data, "dmri")):
if dirname not in ['._.DS_Store', '.DS_Store']:
for filename in os.listdir(
os.path.join(param.path_data, "dmri", dirname)):
if filename.startswith('dmri') and not filename.endswith(
'_seg.nii.gz') and not filename.endswith(
'_detection.nii.gz') and not filename.endswith(
'.vtk'):
print(dirname, filename)
[d_old, d_new], [r_old, r_new] = segmentation(
os.path.join(param.path_data, "dmri", dirname,
filename),
os.path.join(param.path_data, "dmri", dirname),
't1')
if d_old == 0:
d_old = 'OK'
sum_old = sum_old + 1
else:
d_old = 'Not In'
if d_new == 0:
d_new = 'OK'
sum_new = sum_new + 1
else:
d_new = 'Not In'
results_dmri.append([
dirname, d_old, d_new,
round(r_old, 2),
round(r_new, 2)
])
# compute average
results_dmri.append([
'average', sum_old, sum_new,
np.mean([line[3] for line in results_dmri]),
np.mean([line[4] for line in results_dmri])
])
if 't1' in param.data:
print('')
print(
tabulate(results_t1,
headers=[
"Subject-T1", "Detect-old", "Detect-new", "DC-old",
"DC-new"
],
floatfmt=".2f"))
if 't2' in param.data:
print('')
print(
tabulate(results_t2,
headers=[
"Subject-T2", "Detect-old", "Detect-new", "DC-old",
"DC-new"
],
floatfmt=".2f"))
if 'dmri' in param.data:
print('')
print(
tabulate(results_dmri,
headers=[
"Subject-dmri", "Detect-old", "Detect-new", "DC-old",
"DC-new"
],
floatfmt=".2f"))
# display elapsed time
elapsed_time = time.time() - start_time
print('Finished! Elapsed time: ' + str(int(round(elapsed_time))) + 's\n')
# remove temp files
if param.remove_tmp_file:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(param.path_tmp)
e = 0
for i in range(0, len(results_t2)):
if (results_t2[i][4] < 0.8 or results_t2[i][4] < results_t2[i][3]):
e = e + 1
sys.exit(e)
def main(args=None):
# Initialization
# fname_anat = ''
# fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
param = Param()
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-param' in arguments:
param.update(arguments['-param'])
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
verbose = int(arguments['-v'])
# 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
sct.run([
'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:
sct.run([
'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)
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image along z...')
sct.run([
'sct_maths', '-i', 'anat_rpi_straight.nii', '-smooth',
'0,0,' + str(sigma), '-o', 'anat_rpi_straight_smooth.nii'
], verbose)
# 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...'
)
sct.run([
'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 merge_images(list_fname_src, fname_dest, list_fname_warp, param):
"""
Merge multiple source images onto destination space. All images are warped to the destination space and then added.
To deal with overlap during merging (e.g. one voxel in destination image is shared with two input images), the
resulting voxel is divided by the sum of the partial volume of each image. For example, if src(x,y,z)=1 is mapped to
dest(i,j,k) with a partial volume of 0.5 (because destination voxel is bigger), then its value after linear interpolation
will be 0.5. To account for partial volume, the resulting voxel will be: dest(i,j,k) = 0.5*0.5/0.5 = 0.5.
Now, if two voxels overlap in the destination space, let's say: src(x,y,z)=1 and src2'(x',y',z')=1, then the
resulting value will be: dest(i,j,k) = (0.5*0.5 + 0.5*0.5) / (0.5+0.5) = 0.5. So this function acts like a weighted
average operator, only in destination voxels that share multiple source voxels.
Parameters
----------
list_fname_src
fname_dest
list_fname_warp
param
Returns
-------
"""
# create temporary folder
path_tmp = sct.tmp_create()
# get dimensions of destination file
nii_dest = msct_image.Image(fname_dest)
# initialize variables
data = np.zeros([nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2], len(list_fname_src)])
partial_volume = np.zeros([nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2], len(list_fname_src)])
data_merge = np.zeros([nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2]])
# loop across files
i_file = 0
for fname_src in list_fname_src:
# apply transformation src --> dest
sct_apply_transfo.main(args=[
'-i', fname_src,
'-d', fname_dest,
'-w', list_fname_warp[i_file],
'-x', param.interp,
'-o', 'src_' + str(i_file) + '_template.nii.gz',
'-v', param.verbose])
# create binary mask from input file by assigning one to all non-null voxels
sct_maths.main(args=[
'-i', fname_src,
'-bin', str(param.almost_zero),
'-o', 'src_' + str(i_file) + 'native_bin.nii.gz'])
# apply transformation to binary mask to compute partial volume
sct_apply_transfo.main(args=[
'-i', 'src_' + str(i_file) + 'native_bin.nii.gz',
'-d', fname_dest,
'-w', list_fname_warp[i_file],
'-x', param.interp,
'-o', 'src_' + str(i_file) + '_template_partialVolume.nii.gz'])
# open data
data[:, :, :, i_file] = msct_image.Image('src_' + str(i_file) + '_template.nii.gz').data
partial_volume[:, :, :, i_file] = msct_image.Image('src_' + str(i_file) + '_template_partialVolume.nii.gz').data
i_file += 1
# merge files using partial volume information (and convert nan resulting from division by zero to zeros)
data_merge = np.divide(np.sum(data * partial_volume, axis=3), np.sum(partial_volume, axis=3))
data_merge = np.nan_to_num(data_merge)
# write result in file
nii_dest.data = data_merge
nii_dest.save(param.fname_out)
# remove temporary folder
if param.rm_tmp:
sct.rmtree(path_tmp)
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
fname_warp_list = self.warp_input # 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
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 = []
# fname_warp_list = fname_warp_list.replace(' ', '') # remove spaces
# fname_warp_list = fname_warp_list.split(",") # parse with comma
for idx_warp, path_warp in enumerate(fname_warp_list):
# Check if inverse matrix is specified with '-' at the beginning of file name
if path_warp.startswith("-"):
use_inverse.append('-i')
fname_warp_list[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[use_inverse[idx_warp], fname_warp_list[idx_warp]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = fname_warp_list[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and msct_image.Image(fname_warp_list[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# 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 = msct_image.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'
sct.run(['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:
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 fname_warp_list:
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 = msct_image.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 = sct.run(['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 int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv('WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...', verbose, 'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field, background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def merge_images(list_fname_src, fname_dest, list_fname_warp, param):
"""
Merge multiple source images onto destination space. All images are warped to the destination space and then added.
To deal with overlap during merging (e.g. one voxel in destination image is shared with two input images), the
resulting voxel is divided by the sum of the partial volume of each image. For example, if src(x,y,z)=1 is mapped to
dest(i,j,k) with a partial volume of 0.5 (because destination voxel is bigger), then its value after linear interpolation
will be 0.5. To account for partial volume, the resulting voxel will be: dest(i,j,k) = 0.5*0.5/0.5 = 0.5.
Now, if two voxels overlap in the destination space, let's say: src(x,y,z)=1 and src2'(x',y',z')=1, then the
resulting value will be: dest(i,j,k) = (0.5*0.5 + 0.5*0.5) / (0.5+0.5) = 0.5. So this function acts like a weighted
average operator, only in destination voxels that share multiple source voxels.
Parameters
----------
list_fname_src
fname_dest
list_fname_warp
param
Returns
-------
"""
# create temporary folder
path_tmp = sct.tmp_create()
# get dimensions of destination file
nii_dest = msct_image.Image(fname_dest)
# initialize variables
data = np.zeros([
nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2],
len(list_fname_src)
])
partial_volume = np.zeros([
nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2],
len(list_fname_src)
])
data_merge = np.zeros([nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2]])
# loop across files
i_file = 0
for fname_src in list_fname_src:
# apply transformation src --> dest
sct_apply_transfo.main(args=[
'-i', fname_src, '-d', fname_dest, '-w', list_fname_warp[i_file],
'-x', param.interp, '-o', 'src_' + str(i_file) +
'_template.nii.gz', '-v', param.verbose
])
# create binary mask from input file by assigning one to all non-null voxels
sct_maths.main(args=[
'-i', fname_src, '-bin',
str(param.almost_zero), '-o', 'src_' + str(i_file) +
'native_bin.nii.gz'
])
# apply transformation to binary mask to compute partial volume
sct_apply_transfo.main(args=[
'-i', 'src_' + str(i_file) + 'native_bin.nii.gz', '-d', fname_dest,
'-w', list_fname_warp[i_file], '-x', param.interp, '-o', 'src_' +
str(i_file) + '_template_partialVolume.nii.gz'
])
# open data
data[:, :, :, i_file] = msct_image.Image('src_' + str(i_file) +
'_template.nii.gz').data
partial_volume[:, :, :, i_file] = msct_image.Image(
'src_' + str(i_file) + '_template_partialVolume.nii.gz').data
i_file += 1
# merge files using partial volume information (and convert nan resulting from division by zero to zeros)
data_merge = np.divide(np.sum(data * partial_volume, axis=3),
np.sum(partial_volume, axis=3))
data_merge = np.nan_to_num(data_merge)
# write result in file
nii_dest.data = data_merge
nii_dest.save(param.fname_out)
# remove temporary folder
if param.rm_tmp:
sct.rmtree(path_tmp)
def crop_with_gui(self):
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Initialization
fname_data = self.input_filename
suffix_out = '_crop'
remove_temp_files = self.rm_tmp_files
verbose = self.verbose
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' data ................... ' + fname_data)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
path_tmp = sct.tmp_create() + "/"
# copy files into tmp folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
Image('data.nii').change_orientation("RPI").save('data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title('Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.')
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n', 1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.absolutepath = 'data_rpi_crop.nii'
nii.save()
# come back
os.chdir(curdir)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "data_rpi_crop.nii"), os.path.join(path_out, file_out + ext_out))
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
sct.display_viewer_syntax(files=[os.path.join(path_out, file_out + ext_out)])
def main(args=None):
# initializations
initz = ''
initcenter = ''
fname_initlabel = ''
file_labelz = 'labelz.nii.gz'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = os.path.abspath(arguments["-i"])
fname_seg = os.path.abspath(arguments['-s'])
contrast = arguments['-c']
path_template = arguments['-t']
scale_dist = arguments['-scale-dist']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = os.curdir
param.path_qc = arguments.get("-qc", None)
if '-discfile' in arguments:
fname_disc = os.path.abspath(arguments['-discfile'])
else:
fname_disc = None
if '-initz' in arguments:
initz = arguments['-initz']
if '-initcenter' in arguments:
initcenter = arguments['-initcenter']
# if user provided text file, parse and overwrite arguments
if '-initfile' in arguments:
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 '-initlabel' in arguments:
# get absolute path of label
fname_initlabel = os.path.abspath(arguments['-initlabel'])
if '-param' in arguments:
param.update(arguments['-param'][0])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = int(arguments['-r'])
denoise = int(arguments['-denoise'])
laplacian = int(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 = sct.run(['sct_apply_transfo', '-i', 'data.nii', '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', 'data_straight.nii'])
else:
cmd = ['sct_straighten_spinalcord',
'-i', 'data.nii',
'-s', 'segmentation.nii',
'-r', str(remove_temp_files)]
if param.path_qc is not None and os.environ.get("SCT_RECURSIVE_QC", None) == "1":
cmd += ['-qc', param.path_qc]
s, o = sct.run(cmd)
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 = sct.run(['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)
sct.run('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
sct.run(['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)
sct.run('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]
# create single label and output as labels.nii.gz
label = ProcessLabels('segmentation.nii', fname_output='tmp.labelz.nii.gz',
coordinates=['{},{}'.format(initz[0], initz[1])])
im_label = label.process('create-seg')
im_label.data = sct_maths.dilate(im_label.data, [3]) # TODO: create a dilation method specific to labels,
# which does not apply a convolution across all voxels (highly inneficient)
im_label.save(fname_labelz)
elif fname_initlabel:
import sct_label_utils
# subtract "1" to label value because due to legacy, in this code the disc C2-C3 has value "2", whereas in the
# recent version of SCT it is defined as "3". Therefore, when asking the user to define a label, we point to the
# new definition of labels (i.e., C2-C3 = 3).
sct_label_utils.main(['-i', fname_initlabel, '-add', '-1', '-o', fname_labelz])
else:
# automatically finds C2-C3 disc
im_data = Image('data.nii')
im_seg = Image('segmentation.nii')
im_label_c2c3 = detect_c2c3(im_data, im_seg, contrast)
ind_label = np.where(im_label_c2c3.data)
if not np.size(ind_label) == 0:
# subtract "1" to label value because due to legacy, in this code the disc C2-C3 has value "2", whereas in the
# recent version of SCT it is defined as "3".
im_label_c2c3.data[ind_label] = 2
else:
sct.printv('Automatic C2-C3 detection failed. Please provide manual label with sct_label_utils', 1, 'error')
im_label_c2c3.save(fname_labelz)
# dilate label so it is not lost when applying warping
sct_maths.main(['-i', fname_labelz, '-dilate', '3', '-o', fname_labelz])
# Apply straightening to z-label
sct.printv('\nAnd apply straightening to label...', verbose)
sct.run('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)
sct.run(['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)
sct.run(['sct_maths', '-i', 'data_straightr.nii', '-laplacian', '1', '-o', 'data_straightr.nii'], verbose)
# detect vertebral levels on straight spinal cord
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)
sct.run('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)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# 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(param.path_qc)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
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 main(args=None):
if not args:
args = sys.argv[1:]
# initialize parameters
param = Param()
# call main function
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_bvecs = arguments['-bvec']
average = arguments['-a']
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = int(arguments['-r'])
path_out = arguments['-ofolder']
if '-bval' in arguments:
fname_bvals = arguments['-bval']
else:
fname_bvals = ''
if '-bvalmin' in arguments:
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)
sct.run(['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)
sct.run(['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)
sct.generate_output_file(os.path.join(path_tmp, dwi_name + ext), fname_dwi, verbose)
if average:
sct.generate_output_file(os.path.join(path_tmp, b0_mean_name + ext), fname_b0_mean, verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_mean_name + ext), fname_dwi_mean, 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 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)
sct.run(["sct_resample",
"-i", "data.nii",
"-x", "linear",
"-vox", str(nx * interp_factor) + 'x' + str(ny * interp_factor) + 'x' + str(nz * interp_factor),
"-o", "data_up.nii"], verbose)
# Smooth along centerline
sct.printv('\nSmooth along centerline...', verbose)
sct.run(["sct_smooth_spinalcord",
"-i", "data_up.nii",
"-s", "data_up.nii",
"-smooth", str(smoothing_sigma),
"-r", str(remove_temp_files),
"-v", str(verbose)], verbose)
# downsample data
sct.printv('\nDownsample data...', verbose)
sct.run(["sct_resample",
"-i", "data_up_smooth.nii",
"-x", "linear",
"-vox", str(nx) + 'x' + str(ny) + 'x' + str(nz),
"-o", "data_up_smooth_down.nii"], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...')
fname_out = sct.generate_output_file(os.path.join(path_tmp, "data_up_smooth_down.nii"), '' + file_data + suffix + ext_data)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's')
# to view results
sct.printv('\nTo view results, type:')
sct.printv('fslview ' + file_data + ' ' + file_data + suffix + ' &\n')
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialize parameters
param = Param()
# Initialization
fname_output = ''
path_out = ''
fname_src_seg = ''
fname_dest_seg = ''
fname_src_label = ''
fname_dest_label = ''
generate_warpinv = 1
start_time = time.time()
# get path of the toolbox
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
# get default registration parameters
# step1 = Paramreg(step='1', type='im', algo='syn', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5')
step0 = Paramreg(
step='0',
type='im',
algo='syn',
metric='MI',
iter='0',
shrink='1',
smooth='0',
gradStep='0.5',
slicewise='0',
dof='Tx_Ty_Tz_Rx_Ry_Rz') # only used to put src into dest space
step1 = Paramreg(step='1', type='im')
paramreg = ParamregMultiStep([step0, step1])
parser = get_parser(paramreg=paramreg)
arguments = parser.parse(args)
# get arguments
fname_src = arguments['-i']
fname_dest = arguments['-d']
if '-iseg' in arguments:
fname_src_seg = arguments['-iseg']
if '-dseg' in arguments:
fname_dest_seg = arguments['-dseg']
if '-ilabel' in arguments:
fname_src_label = arguments['-ilabel']
if '-dlabel' in arguments:
fname_dest_label = arguments['-dlabel']
if '-o' in arguments:
fname_output = arguments['-o']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-owarp' in arguments:
fname_output_warp = arguments['-owarp']
else:
fname_output_warp = ''
if '-initwarp' in arguments:
fname_initwarp = os.path.abspath(arguments['-initwarp'])
else:
fname_initwarp = ''
if '-initwarpinv' in arguments:
fname_initwarpinv = os.path.abspath(arguments['-initwarpinv'])
else:
fname_initwarpinv = ''
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
padding = arguments['-z']
if "-param" in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
path_qc = arguments.get("-qc", None)
identity = int(arguments['-identity'])
interp = arguments['-x']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# sct.printv(arguments)
sct.printv('\nInput parameters:')
sct.printv(' Source .............. ' + fname_src)
sct.printv(' Destination ......... ' + fname_dest)
sct.printv(' Init transfo ........ ' + fname_initwarp)
sct.printv(' Mask ................ ' + fname_mask)
sct.printv(' Output name ......... ' + fname_output)
# sct.printv(' Algorithm ........... '+paramreg.algo)
# sct.printv(' Number of iterations '+paramreg.iter)
# sct.printv(' Metric .............. '+paramreg.metric)
sct.printv(' Remove temp files ... ' + str(remove_temp_files))
sct.printv(' Verbose ............. ' + str(verbose))
# update param
param.verbose = verbose
param.padding = padding
param.fname_mask = fname_mask
param.remove_temp_files = remove_temp_files
# Get if input is 3D
sct.printv('\nCheck if input data are 3D...', verbose)
sct.check_if_3d(fname_src)
sct.check_if_3d(fname_dest)
# Check if user selected type=seg, but did not input segmentation data
if 'paramreg_user' in locals():
if True in [
'type=seg' in paramreg_user[i]
for i in range(len(paramreg_user))
]:
if fname_src_seg == '' or fname_dest_seg == '':
sct.printv(
'\nERROR: if you select type=seg you must specify -iseg and -dseg flags.\n',
1, 'error')
# 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)
# check if source and destination images have the same name (related to issue #373)
# If so, change names to avoid conflict of result files and warns the user
suffix_src, suffix_dest = '_reg', '_reg'
if file_src == file_dest:
suffix_src, suffix_dest = '_src_reg', '_dest_reg'
# define output folder and file name
if fname_output == '':
path_out = '' if not path_out else path_out # output in user's current directory
file_out = file_src + suffix_src
file_out_inv = file_dest + suffix_dest
ext_out = ext_src
else:
path, file_out, ext_out = sct.extract_fname(fname_output)
path_out = path if not path_out else path_out
file_out_inv = file_out + '_inv'
# create temporary folder
path_tmp = sct.tmp_create()
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
Image(fname_src).save(os.path.join(path_tmp, "src.nii"))
Image(fname_dest).save(os.path.join(path_tmp, "dest.nii"))
if fname_src_seg:
Image(fname_src_seg).save(os.path.join(path_tmp, "src_seg.nii"))
if fname_dest_seg:
Image(fname_dest_seg).save(os.path.join(path_tmp, "dest_seg.nii"))
if fname_src_label:
Image(fname_src_label).save(os.path.join(path_tmp, "src_label.nii"))
Image(fname_dest_label).save(os.path.join(path_tmp, "dest_label.nii"))
if fname_mask != '':
Image(fname_mask).save(os.path.join(path_tmp, "mask.nii.gz"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# reorient destination to RPI
Image('dest.nii').change_orientation("RPI").save('dest_RPI.nii')
if fname_dest_seg:
Image('dest_seg.nii').change_orientation("RPI").save(
'dest_seg_RPI.nii')
if fname_dest_label:
Image('dest_label.nii').change_orientation("RPI").save(
'dest_label_RPI.nii')
if identity:
# overwrite paramreg and only do one identity transformation
step0 = Paramreg(step='0',
type='im',
algo='syn',
metric='MI',
iter='0',
shrink='1',
smooth='0',
gradStep='0.5')
paramreg = ParamregMultiStep([step0])
# Put source into destination space using header (no estimation -- purely based on header)
# TODO: Check if necessary to do that
# TODO: use that as step=0
# sct.printv('\nPut source into destination space using header...', verbose)
# sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[dest_pad.nii,src.nii,1,16] -c 0 -f 1 -s 0 -o
# [regAffine,src_regAffine.nii] -n BSpline[3]', verbose)
# if segmentation, also do it for seg
# initialize list of warping fields
warp_forward = []
warp_inverse = []
# initial warping is specified, update list of warping fields and skip step=0
if fname_initwarp:
sct.printv('\nSkip step=0 and replace with initial transformations: ',
param.verbose)
sct.printv(' ' + fname_initwarp, param.verbose)
# sct.copy(fname_initwarp, 'warp_forward_0.nii.gz')
warp_forward = [fname_initwarp]
start_step = 1
if fname_initwarpinv:
warp_inverse = [fname_initwarpinv]
else:
sct.printv(
'\nWARNING: No initial inverse warping field was specified, therefore the inverse warping field '
'will NOT be generated.', param.verbose, 'warning')
generate_warpinv = 0
else:
start_step = 0
# loop across registration steps
for i_step in range(start_step, len(paramreg.steps)):
sct.printv('\n--\nESTIMATE TRANSFORMATION FOR STEP #' + str(i_step),
param.verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = 'src.nii'
dest = 'dest_RPI.nii'
interp_step = 'spline'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'src_seg.nii'
dest = 'dest_seg_RPI.nii'
interp_step = 'nn'
elif paramreg.steps[str(i_step)].type == 'label':
src = 'src_label.nii'
dest = 'dest_label_RPI.nii'
interp_step = 'nn'
else:
# src = dest = interp_step = None
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>0, apply warp_forward_concat to the src image to be used
if i_step > 0:
sct.printv('\nApply transformation from previous step',
param.verbose)
sct.run([
'sct_apply_transfo', '-i', src, '-d', dest, '-w',
','.join(warp_forward), '-o',
sct.add_suffix(src, '_reg'), '-x', interp_step
], verbose)
src = sct.add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg,
param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations
sct.printv('\nConcatenate transformations...', verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_forward), '-d', 'dest.nii',
'-o', 'warp_src2dest.nii.gz'
], verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse), '-d', 'src.nii',
'-o', 'warp_dest2src.nii.gz'
], verbose)
# Apply warping field to src data
sct.printv('\nApply transfo source --> dest...', verbose)
sct.run([
'sct_apply_transfo', '-i', 'src.nii', '-o', 'src_reg.nii', '-d',
'dest.nii', '-w', 'warp_src2dest.nii.gz', '-x', interp
], verbose)
sct.printv('\nApply transfo dest --> source...', verbose)
sct.run([
'sct_apply_transfo', '-i', 'dest.nii', '-o', 'dest_reg.nii', '-d',
'src.nii', '-w', 'warp_dest2src.nii.gz', '-x', interp
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
# generate: src_reg
fname_src2dest = sct.generate_output_file(
os.path.join(path_tmp, "src_reg.nii"),
os.path.join(path_out, file_out + ext_out), verbose)
# generate: forward warping field
if fname_output_warp == '':
fname_output_warp = os.path.join(
path_out, 'warp_' + file_src + '2' + file_dest + '.nii.gz')
sct.generate_output_file(os.path.join(path_tmp, "warp_src2dest.nii.gz"),
fname_output_warp, verbose)
if generate_warpinv:
# generate: dest_reg
fname_dest2src = sct.generate_output_file(
os.path.join(path_tmp, "dest_reg.nii"),
os.path.join(path_out, file_out_inv + ext_dest), verbose)
# generate: inverse warping field
sct.generate_output_file(
os.path.join(path_tmp, "warp_dest2src.nii.gz"),
os.path.join(path_out,
'warp_' + file_dest + '2' + file_src + '.nii.gz'),
verbose)
# Delete temporary files
if remove_temp_files:
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)
if path_qc is not None:
if fname_dest_seg:
generate_qc(fname_src2dest,
fname_in2=fname_dest,
fname_seg=fname_dest_seg,
args=args,
path_qc=os.path.abspath(path_qc),
process='sct_register_multimodal')
else:
sct.printv(
'WARNING: Cannot generate QC because it requires destination segmentation.',
1, 'warning')
if generate_warpinv:
sct.display_viewer_syntax([fname_src, fname_dest2src], verbose=verbose)
sct.display_viewer_syntax([fname_dest, fname_src2dest], verbose=verbose)
def main(args=None):
# initialization
start_time = time.time()
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' input file ............' + param.fname_data, param.verbose)
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_tmp = sct.tmp_create(basename="fmri_moco", verbose=param.verbose)
# Copying input data to tmp folder and convert to nii
# TODO: no need to do that (takes time for nothing)
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, os.path.join(path_tmp, "fmri.nii"), squeeze_data=False)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# run moco
fmri_moco(param)
# come back
os.chdir(curdir)
# Generate output files
fname_fmri_moco = os.path.join(path_out, file_data + param.suffix + ext_data)
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(os.path.join(path_tmp, "fmri" + param.suffix + '.nii'), fname_fmri_moco, param.verbose)
sct.generate_output_file(os.path.join(path_tmp, "fmri" + param.suffix + '_mean.nii'), os.path.join(path_out, file_data + param.suffix + '_mean' + ext_data), param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', param.verbose)
sct.display_viewer_syntax([fname_fmri_moco, file_data], mode='ortho,ortho')
def segmentation(fname_input, output_dir, image_type):
# parameters
path_in, file_in, ext_in = sct.extract_fname(fname_input)
segmentation_filename_old = os.path.join(path_in, 'old',
file_in + '_seg' + ext_in)
manual_segmentation_filename_old = os.path.join(
path_in, 'manual_' + file_in + ext_in)
detection_filename_old = os.path.join(path_in, 'old',
file_in + '_detection' + ext_in)
segmentation_filename_new = os.path.join(path_in, 'new',
file_in + '_seg' + ext_in)
manual_segmentation_filename_new = os.path.join(
path_in, 'manual_' + file_in + ext_in)
detection_filename_new = os.path.join(path_in, 'new',
file_in + '_detection' + ext_in)
# initialize results of segmentation and detection
results_detection = [0, 0]
results_segmentation = [0.0, 0.0]
# perform PropSeg old version
sct.rmtree(os.path.join(output_dir, 'old'))
sct.create_folder(os.path.join(output_dir, 'old'))
cmd = 'sct_propseg_old -i ' + fname_input \
+ ' -o ' + os.path.join(output_dir, 'old') \
+ ' -t ' + image_type \
+ ' -detect-nii'
sct.printv(cmd)
status_propseg_old, output_propseg_old = sct.run(cmd)
sct.printv(output_propseg_old)
# check if spinal cord is correctly detected with old version of PropSeg
cmd = "isct_check_detection.py -i " + detection_filename_old + " -t " + manual_segmentation_filename_old
sct.printv(cmd)
status_detection_old, output_detection_old = sct.run(cmd)
sct.printv(output_detection_old)
results_detection[0] = status_detection_old
# compute Dice coefficient for old version of PropSeg
cmd_validation = 'sct_dice_coefficient '+segmentation_filename_old \
+ ' '+manual_segmentation_filename_old \
+ ' -bzmax'
sct.printv(cmd_validation)
status_validation_old, output_validation_old = sct.run(cmd_validation)
print(output_validation_old)
res = output_validation_old.split()[-1]
if res != 'nan': results_segmentation[0] = float(res)
else: results_segmentation[0] = 0.0
# perform PropSeg new version
sct.rmtree(os.path.join(output_dir, 'new'))
sct.create_folder(os.path.join(output_dir, 'new'))
cmd = 'sct_propseg -i ' + fname_input \
+ ' -o ' + os.path.join(output_dir, 'new') \
+ ' -t ' + image_type \
+ ' -detect-nii'
sct.printv(cmd)
status_propseg_new, output_propseg_new = sct.run(cmd)
sct.printv(output_propseg_new)
# check if spinal cord is correctly detected with new version of PropSeg
cmd = "isct_check_detection.py -i " + detection_filename_new + " -t " + manual_segmentation_filename_new
sct.printv(cmd)
status_detection_new, output_detection_new = sct.run(cmd)
sct.printv(output_detection_new)
results_detection[1] = status_detection_new
# compute Dice coefficient for new version of PropSeg
cmd_validation = 'sct_dice_coefficient '+segmentation_filename_new \
+ ' '+manual_segmentation_filename_new \
+ ' -bzmax'
sct.printv(cmd_validation)
status_validation_new, output_validation_new = sct.run(cmd_validation)
print(output_validation_new)
res = output_validation_new.split()[-1]
if res != 'nan': results_segmentation[1] = float(res)
else: results_segmentation[1] = 0.0
return results_detection, results_segmentation
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialization
# note: mirror servers are listed in order of priority
dict_url = {
'sct_example_data': ['https://osf.io/kjcgs/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180525_sct_example_data.zip'],
'sct_testing_data': ['https://osf.io/z8gaj/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180125_sct_testing_data.zip'],
'PAM50': ['https://osf.io/kc3jx/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20181214_PAM50.zip'],
'MNI-Poly-AMU': ['https://osf.io/sh6h4/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170310_MNI-Poly-AMU.zip'],
'gm_model': ['https://osf.io/ugscu/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20160922_gm_model.zip'],
'optic_models': ['https://osf.io/g4fwn/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170413_optic_models.zip'],
'pmj_models': ['https://osf.io/4gufr/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20170922_pmj_models.zip'],
'binaries_debian': ['https://osf.io/z72vn/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20181204_sct_binaries_linux.tar.gz'],
'binaries_centos': ['https://osf.io/97ybd/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20181204_sct_binaries_linux_centos6.tar.gz'],
'binaries_osx': ['https://osf.io/zjv4c/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20181204_sct_binaries_osx.tar.gz'],
'course_hawaii17': 'https://osf.io/6exht/?action=download',
'course_paris18': ['https://osf.io/9bmn5/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180612_sct_course-paris18.zip'],
'course_london19': ['https://osf.io/4q3u7/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20190121_sct_course-london19.zip'],
'deepseg_gm_models': ['https://osf.io/b9y4x/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180205_deepseg_gm_models.zip'],
'deepseg_sc_models': ['https://osf.io/avf97/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180610_deepseg_sc_models.zip'],
'deepseg_lesion_models': ['https://osf.io/eg7v9/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20180613_deepseg_lesion_models.zip'],
'c2c3_disc_models': ['https://osf.io/t97ap/?action=download',
'https://www.neuro.polymtl.ca/_media/downloads/sct/20190117_c2c3_disc_models.zip']
}
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
data_name = arguments['-d']
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
dest_folder = arguments.get('-o', os.path.abspath(os.curdir))
# Download data
url = dict_url[data_name]
tmp_file = download_data(url, verbose)
# Check if folder already exists
sct.printv('\nCheck if folder already exists...', verbose)
if os.path.isdir(data_name):
sct.printv('WARNING: Folder ' + data_name + ' already exists. Removing it...', 1, 'warning')
sct.rmtree(data_name)
# unzip
unzip(tmp_file, dest_folder, verbose)
sct.printv('\nRemove temporary file...', verbose)
os.remove(tmp_file)
sct.printv('Done!\n', verbose)
return 0
def main(args=None):
# Initialization
param = Param()
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-param' in arguments:
param.update(arguments['-param'])
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
verbose = int(arguments.get('-v'))
sct.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
sct.run(['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:
sct.run(['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...')
sct.run(['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 pre_processing(fname_target, fname_sc_seg, fname_level=None, fname_manual_gmseg=None, new_res=0.3, square_size_size_mm=22.5, denoising=True, verbose=1, rm_tmp=True, for_model=False):
printv('\nPre-process data...', verbose, 'normal')
tmp_dir = sct.tmp_create()
sct.copy(fname_target, tmp_dir)
fname_target = ''.join(extract_fname(fname_target)[1:])
sct.copy(fname_sc_seg, tmp_dir)
fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir)
original_info = {'orientation': None, 'im_sc_seg_rpi': None, 'interpolated_images': []}
im_target = Image(fname_target).copy()
im_sc_seg = Image(fname_sc_seg).copy()
# get original orientation
printv(' Reorient...', verbose, 'normal')
original_info['orientation'] = im_target.orientation
# assert images are in the same orientation
assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation"
im_target_rpi = im_target.copy().change_orientation('RPI', generate_path=True).save()
im_sc_seg_rpi = im_sc_seg.copy().change_orientation('RPI', generate_path=True).save()
original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy() # target image in RPI will be used to post-process segmentations
# denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy
if denoising:
printv(' Denoise...', verbose, 'normal')
# crop image before denoising to fasten denoising
nx, ny, nz, nt, px, py, pz, pt = im_target_rpi.dim
size_x, size_y = (square_size_size_mm + 1) / px, (square_size_size_mm + 1) / py
size = int(np.ceil(max(size_x, size_y)))
# create mask
fname_mask = 'mask_pre_crop.nii.gz'
sct_create_mask.main(['-i', im_target_rpi.absolutepath, '-p', 'centerline,' + im_sc_seg_rpi.absolutepath, '-f', 'box', '-size', str(size), '-o', fname_mask])
# crop image
fname_target_crop = add_suffix(im_target_rpi.absolutepath, '_pre_crop')
crop_im = ImageCropper(input_file=im_target_rpi.absolutepath, output_file=fname_target_crop, mask=fname_mask)
im_target_rpi_crop = crop_im.crop()
# crop segmentation
fname_sc_seg_crop = add_suffix(im_sc_seg_rpi.absolutepath, '_pre_crop')
crop_sc_seg = ImageCropper(input_file=im_sc_seg_rpi.absolutepath, output_file=fname_sc_seg_crop, mask=fname_mask)
im_sc_seg_rpi_crop = crop_sc_seg.crop()
# denoising
from sct_maths import denoise_nlmeans
block_radius = 3
block_radius = int(im_target_rpi_crop.data.shape[2] / 2) if im_target_rpi_crop.data.shape[2] < (block_radius*2) else block_radius
patch_radius = block_radius -1
data_denoised = denoise_nlmeans(im_target_rpi_crop.data, block_radius=block_radius, patch_radius=patch_radius)
im_target_rpi_crop.data = data_denoised
im_target_rpi = im_target_rpi_crop
im_sc_seg_rpi = im_sc_seg_rpi_crop
else:
fname_mask = None
# interpolate image to reference square image (resample and square crop centered on SC)
printv(' Interpolate data to the model space...', verbose, 'normal')
list_im_slices = interpolate_im_to_ref(im_target_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm)
original_info['interpolated_images'] = list_im_slices # list of images (not Slice() objects)
printv(' Mask data using the spinal cord segmentation...', verbose, 'normal')
list_sc_seg_slices = interpolate_im_to_ref(im_sc_seg_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=1)
for i in range(len(list_im_slices)):
# list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0
list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i], thr_min=0.5, thr_max=1)
list_im_slices[i].data = list_im_slices[i].data * list_sc_seg_slices[i].data
printv(' Split along rostro-caudal direction...', verbose, 'normal')
list_slices_target = [Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[]) for i, im_slice in enumerate(list_im_slices)]
# load vertebral levels
if fname_level is not None:
printv(' Load vertebral levels...', verbose, 'normal')
# copy level file to tmp dir
os.chdir(curdir)
sct.copy(fname_level, tmp_dir)
os.chdir(tmp_dir)
# change fname level to only file name (path = tmp dir now)
fname_level = ''.join(extract_fname(fname_level)[1:])
# load levels
list_slices_target = load_level(list_slices_target, fname_level)
os.chdir(curdir)
# load manual gmseg if there is one (model data)
if fname_manual_gmseg is not None:
printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal')
list_slices_target = load_manual_gmseg(list_slices_target, fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=for_model, fname_mask=fname_mask)
if rm_tmp:
# remove tmp folder
sct.rmtree(tmp_dir)
return list_slices_target, original_info