def load_manual_gmseg(list_slices_target,
list_fname_manual_gmseg,
tmp_dir,
im_sc_seg_rpi,
new_res,
square_size_size_mm,
for_model=False,
fname_mask=None):
if isinstance(list_fname_manual_gmseg, str):
# consider fname_manual_gmseg as a list of file names to allow multiple manual GM segmentation
list_fname_manual_gmseg = [list_fname_manual_gmseg]
curdir = os.getcwd()
for fname_manual_gmseg in list_fname_manual_gmseg:
sct.copy(fname_manual_gmseg, tmp_dir)
# change fname level to only file name (path = tmp dir now)
path_gm, file_gm, ext_gm = extract_fname(fname_manual_gmseg)
fname_manual_gmseg = file_gm + ext_gm
os.chdir(tmp_dir)
im_manual_gmseg = Image(fname_manual_gmseg).change_orientation("RPI")
if fname_mask is not None:
fname_gmseg_crop = add_suffix(im_manual_gmseg.absolutepath,
'_pre_crop')
crop_im = ImageCropper(input_file=im_manual_gmseg.absolutepath,
output_file=fname_gmseg_crop,
mask=fname_mask)
im_manual_gmseg_crop = crop_im.crop()
im_manual_gmseg = im_manual_gmseg_crop
# assert gmseg has the right number of slices
assert im_manual_gmseg.data.shape[2] == len(
list_slices_target
), 'ERROR: the manual GM segmentation has not the same number of slices than the image.'
# interpolate gm to reference image
nz_gmseg, nx_gmseg, ny_gmseg, nt_gmseg, pz_gmseg, px_gmseg, py_gmseg, pt_gmseg = im_manual_gmseg.dim
list_im_gm = interpolate_im_to_ref(im_manual_gmseg,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm,
interpolation_mode=0)
# load gm seg in list of slices
n_poped = 0
for im_gm, slice_im in zip(list_im_gm, list_slices_target):
if im_gm.data.max() == 0 and for_model:
list_slices_target.pop(slice_im.id - n_poped)
n_poped += 1
else:
slice_im.gm_seg.append(im_gm.data)
wm_slice = (slice_im.im > 0) - im_gm.data
slice_im.wm_seg.append(wm_slice)
os.chdir(curdir)
return list_slices_target
def ifolder2tmp(self):
# copy input image
if self.fname_mask is not None:
sct.copy(self.fname_mask, self.tmp_dir)
self.fname_mask = ''.join(extract_fname(self.fname_mask)[1:])
else:
printv('ERROR: No input image', self.verbose, 'error')
# copy seg image
if self.fname_sc is not None:
sct.copy(self.fname_sc, self.tmp_dir)
self.fname_sc = ''.join(extract_fname(self.fname_sc)[1:])
# copy ref image
if self.fname_ref is not None:
sct.copy(self.fname_ref, self.tmp_dir)
self.fname_ref = ''.join(extract_fname(self.fname_ref)[1:])
# copy registered template
if self.path_template is not None:
sct.copy(self.path_levels, self.tmp_dir)
self.path_levels = ''.join(extract_fname(self.path_levels)[1:])
self.atlas_roi_lst = []
for fname_atlas_roi in os.listdir(self.path_atlas):
if fname_atlas_roi.endswith('.nii.gz'):
tract_id = int(
fname_atlas_roi.split('_')[-1].split('.nii.gz')[0])
if tract_id < 36: # Not interested in CSF
sct.copy(
os.path.join(self.path_atlas, fname_atlas_roi),
self.tmp_dir)
self.atlas_roi_lst.append(fname_atlas_roi)
os.chdir(self.tmp_dir) # go to tmp directory
def warp_label(path_label, folder_label, file_label, fname_src, fname_transfo, path_out):
"""
Warp label files according to info_label.txt file
:param path_label:
:param folder_label:
:param file_label:
:param fname_src:
:param fname_transfo:
:param path_out:
:return:
"""
# read label file and check if file exists
sct.printv('\nRead label file...', param.verbose)
try:
template_label_ids, template_label_names, template_label_file, combined_labels_ids, combined_labels_names, combined_labels_id_groups, clusters_apriori = spinalcordtoolbox.metadata.read_label_file(os.path.join(path_label, folder_label), file_label)
except Exception as error:
sct.printv('\nWARNING: Cannot warp label ' + folder_label + ': ' + str(error), 1, 'warning')
raise
else:
# create output folder
if not os.path.exists(os.path.join(path_out, folder_label)):
os.makedirs(os.path.join(path_out, folder_label))
# Warp label
for i in range(0, len(template_label_file)):
fname_label = os.path.join(path_label, folder_label, template_label_file[i])
# check if file exists
# sct.check_file_exist(fname_label)
# apply transfo
sct.run('sct_apply_transfo -i ' + fname_label + ' -o ' + os.path.join(path_out, folder_label, template_label_file[i]) + ' -d ' + fname_src + ' -w ' + fname_transfo + ' -x ' + get_interp(template_label_file[i]), param.verbose)
# Copy list.txt
sct.copy(os.path.join(path_label, folder_label, param.file_info_label), os.path.join(path_out, folder_label))
def create_line(param, fname, coord, nz):
"""
Create vertical line in 3D volume
:param param:
:param fname:
:param coord:
:param nz:
:return:
"""
# duplicate volume (assumes input file is nifti)
sct.copy(fname, 'line.nii', verbose=param.verbose)
# set all voxels to zero
sct.run(['sct_maths', '-i', 'line.nii', '-mul', '0', '-o', 'line.nii'], param.verbose)
cmd = ['sct_label_utils', '-i', 'line.nii', '-o', 'line.nii', '-create-add']
for iz in range(nz):
if iz == nz - 1:
cmd += [str(int(coord[0])) + ',' + str(int(coord[1])) + ',' + str(iz) + ',1']
else:
cmd += [str(int(coord[0])) + ',' + str(int(coord[1])) + ',' + str(iz) + ',1:']
sct.run(cmd, param.verbose)
return 'line.nii'
def create_line(param, fname, coord, nz):
"""
Create vertical line in 3D volume
:param param:
:param fname:
:param coord:
:param nz:
:return:
"""
# duplicate volume (assumes input file is nifti)
sct.copy(fname, 'line.nii', verbose=param.verbose)
# set all voxels to zero
sct.run(['sct_maths', '-i', 'line.nii', '-mul', '0', '-o', 'line.nii'],
param.verbose)
cmd = [
'sct_label_utils', '-i', 'line.nii', '-o', 'line.nii', '-create-add'
]
for iz in range(nz):
if iz == nz - 1:
cmd += [
str(int(coord[0])) + ',' + str(int(coord[1])) + ',' + str(iz) +
',1'
]
else:
cmd += [
str(int(coord[0])) + ',' + str(int(coord[1])) + ',' + str(iz) +
',1:'
]
sct.run(cmd, param.verbose)
return 'line.nii'
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 ifolder2tmp(self):
# copy input image
if self.fname_mask is not None:
sct.copy(self.fname_mask, self.tmp_dir)
self.fname_mask = ''.join(extract_fname(self.fname_mask)[1:])
else:
printv('ERROR: No input image', self.verbose, 'error')
# copy seg image
if self.fname_sc is not None:
sct.copy(self.fname_sc, self.tmp_dir)
self.fname_sc = ''.join(extract_fname(self.fname_sc)[1:])
# copy ref image
if self.fname_ref is not None:
sct.copy(self.fname_ref, self.tmp_dir)
self.fname_ref = ''.join(extract_fname(self.fname_ref)[1:])
# copy registered template
if self.path_template is not None:
sct.copy(self.path_levels, self.tmp_dir)
self.path_levels = ''.join(extract_fname(self.path_levels)[1:])
self.atlas_roi_lst = []
for fname_atlas_roi in os.listdir(self.path_atlas):
if fname_atlas_roi.endswith('.nii.gz'):
tract_id = int(fname_atlas_roi.split('_')[-1].split('.nii.gz')[0])
if tract_id < 36: # Not interested in CSF
sct.copy(os.path.join(self.path_atlas, fname_atlas_roi), self.tmp_dir)
self.atlas_roi_lst.append(fname_atlas_roi)
os.chdir(self.tmp_dir) # go to tmp directory
def create_line(param, fname, coord, nz):
"""
Create vertical line in 3D volume
:param param:
:param fname:
:param coord:
:param nz:
:return:
"""
# duplicate volume (assumes input file is nifti)
sct.copy(fname, 'line.nii', verbose=param.verbose)
# set all voxels to zero
run_proc(['sct_maths', '-i', 'line.nii', '-mul', '0', '-o', 'line.nii'],
param.verbose)
labels = []
if isinstance(coord[0], Coordinate):
for x, y, _, _ in coord:
labels.extend([Coordinate([x, y, iz, 1]) for iz in range(nz)])
else:
# backwards compat
labels.extend(
[Coordinate([coord[0], coord[1], iz, 1]) for iz in range(nz)])
create_labels(Image("line.nii"), labels).save(path="line.nii")
return 'line.nii'
def 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 tmp2ofolder(self):
os.chdir(self.wrk_dir) # go back to working directory
printv('\nSave results files...', self.verbose, 'normal')
printv('\n... measures saved in the files:', self.verbose, 'normal')
for file_ in [self.fname_label, self.excel_name, self.pickle_name]:
printv('\n - ' + os.path.join(self.path_ofolder, file_), self.verbose, 'normal')
sct.copy(os.path.join(self.tmp_dir, file_), os.path.join(self.path_ofolder, file_))
def tmp2ofolder(self):
os.chdir(self.curdir) # go back to original directory
sct.printv('\nSave resulting files...', self.param.verbose, 'normal')
for f in self.fname_metric_lst: # Copy from tmp folder to ofolder
sct.copy(os.path.join(self.tmp_dir, self.fname_metric_lst[f]),
os.path.join(self.param.path_results, self.fname_metric_lst[f]))
def tmp2ofolder(self):
os.chdir(self.curdir) # go back to original directory
printv('\nSave resulting files...', self.param.verbose, 'normal')
for f in self.fname_metric_lst: # Copy from tmp folder to ofolder
sct.copy(os.path.join(self.tmp_dir, self.fname_metric_lst[f]),
os.path.join(self.param.path_results, self.fname_metric_lst[f]))
def tmp2ofolder(self):
os.chdir(self.wrk_dir) # go back to working directory
printv('\nSave results files...', self.verbose, 'normal')
printv('\n... measures saved in the files:', self.verbose, 'normal')
for file_ in [self.fname_label, self.excel_name, self.pickle_name]:
printv('\n - ' + os.path.join(self.path_ofolder, file_),
self.verbose, 'normal')
sct.copy(os.path.join(self.tmp_dir, file_),
os.path.join(self.path_ofolder, file_))
def tmp2ofolder(self):
"""Copy output files to the ofolder."""
os.chdir(self.curdir) # go back to original directory
if self.pa_coord != -1: # If PMJ has been detected
sct.printv('\nSave resulting file...', self.verbose, 'normal')
sct.copy(os.path.abspath(os.path.join(self.tmp_dir, self.fname_out)),
os.path.abspath(os.path.join(self.path_out, self.fname_out)))
return os.path.join(self.path_out, self.fname_out)
else:
return None
def tmp2ofolder(self):
"""Copy output files to the ofolder."""
os.chdir(self.curdir) # go back to original directory
if self.pa_coord != -1: # If PMJ has been detected
sct.printv('\nSave resulting file...', self.verbose, 'normal')
sct.copy(
os.path.abspath(os.path.join(self.tmp_dir, self.fname_out)),
os.path.abspath(os.path.join(self.path_out, self.fname_out)))
return os.path.join(self.path_out, self.fname_out)
else:
return None
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 centerline2roi(fname_image, folder_output='./', verbose=0):
"""
Tis method converts a binary centerline image to a .roi centerline file
:param fname_image: filename of the binary centerline image, in RPI orientation
:param folder_output: path to output folder where to copy .roi centerline
:param verbose: adjusts the verbosity of the logging.
:returns: filename of the .roi centerline that has been created
"""
# TODO: change folder_output to fname_out
path_data, file_data, ext_data = sct.extract_fname(fname_image)
fname_output = file_data + '.roi'
date_now = datetime.datetime.now()
ROI_TEMPLATE = 'Begin Marker ROI\n' \
' Build version="7.0_33"\n' \
' Annotation=""\n' \
' Colour=0\n' \
' Image source="{fname_segmentation}"\n' \
' Created "{creation_date}" by Operator ID="SCT"\n' \
' Slice={slice_num}\n' \
' Begin Shape\n' \
' X={position_x}; Y={position_y}\n' \
' End Shape\n' \
'End Marker ROI\n'
im = Image(fname_image)
nx, ny, nz, nt, px, py, pz, pt = im.dim
coordinates_centerline = im.getNonZeroCoordinates(sorting='z')
f = open(fname_output, "w")
sct.printv('\nWriting ROI file...', verbose)
for coord in coordinates_centerline:
coord_phys_center = im.transfo_pix2phys([[(nx - 1) / 2.0,
(ny - 1) / 2.0, coord.z]])[0]
coord_phys = im.transfo_pix2phys([[coord.x, coord.y, coord.z]])[0]
f.write(
ROI_TEMPLATE.format(
fname_segmentation=fname_image,
creation_date=date_now.strftime("%d %B %Y %H:%M:%S.%f %Z"),
slice_num=coord.z + 1,
position_x=coord_phys_center[0] - coord_phys[0],
position_y=coord_phys_center[1] - coord_phys[1]))
f.close()
if os.path.abspath(folder_output) != os.getcwd():
sct.copy(fname_output, folder_output)
return fname_output
def ifolder2tmp(self):
"""Copy data to tmp folder."""
if self.fname_im is not None: # copy input image
sct.copy(self.fname_im, self.tmp_dir)
self.fname_im = ''.join(sct.extract_fname(self.fname_im)[1:])
else:
sct.printv('ERROR: No input image', self.verbose, 'error')
if self.fname_seg is not None: # copy segmentation image
sct.copy(self.fname_seg, self.tmp_dir)
self.fname_seg = ''.join(sct.extract_fname(self.fname_seg)[1:])
self.curdir = os.getcwd()
os.chdir(self.tmp_dir) # go to tmp directory
def ifolder2tmp(self):
"""Copy data to tmp folder."""
if self.fname_im is not None: # copy input image
sct.copy(self.fname_im, self.tmp_dir)
self.fname_im = ''.join(sct.extract_fname(self.fname_im)[1:])
else:
sct.printv('ERROR: No input image', self.verbose, 'error')
if self.fname_seg is not None: # copy segmentation image
sct.copy(self.fname_seg, self.tmp_dir)
self.fname_seg = ''.join(sct.extract_fname(self.fname_seg)[1:])
self.curdir = os.getcwd()
os.chdir(self.tmp_dir) # go to tmp directory
def copy_data_to_tmp(self):
# copy input image
if self.param_seg.fname_im is not None:
sct.copy(self.param_seg.fname_im, self.tmp_dir)
self.param_seg.fname_im = ''.join(
extract_fname(self.param_seg.fname_im)[1:])
else:
printv('ERROR: No input image', self.param.verbose, 'error')
# copy sc seg image
if self.param_seg.fname_seg is not None:
sct.copy(self.param_seg.fname_seg, self.tmp_dir)
self.param_seg.fname_seg = ''.join(
extract_fname(self.param_seg.fname_seg)[1:])
else:
printv('ERROR: No SC segmentation image', self.param.verbose,
'error')
# copy level file
if self.param_seg.fname_level is not None:
sct.copy(self.param_seg.fname_level, self.tmp_dir)
self.param_seg.fname_level = ''.join(
extract_fname(self.param_seg.fname_level)[1:])
if self.param_seg.fname_manual_gmseg is not None:
sct.copy(self.param_seg.fname_manual_gmseg, self.tmp_dir)
self.param_seg.fname_manual_gmseg = ''.join(
extract_fname(self.param_seg.fname_manual_gmseg)[1:])
def centerline2roi(fname_image, folder_output='./', verbose=0):
"""
Tis method converts a binary centerline image to a .roi centerline file
:param fname_image: filename of the binary centerline image, in RPI orientation
:param folder_output: path to output folder where to copy .roi centerline
:param verbose: adjusts the verbosity of the logging.
:returns: filename of the .roi centerline that has been created
"""
# TODO: change folder_output to fname_out
path_data, file_data, ext_data = sct.extract_fname(fname_image)
fname_output = file_data + '.roi'
date_now = datetime.datetime.now()
ROI_TEMPLATE = 'Begin Marker ROI\n' \
' Build version="7.0_33"\n' \
' Annotation=""\n' \
' Colour=0\n' \
' Image source="{fname_segmentation}"\n' \
' Created "{creation_date}" by Operator ID="SCT"\n' \
' Slice={slice_num}\n' \
' Begin Shape\n' \
' X={position_x}; Y={position_y}\n' \
' End Shape\n' \
'End Marker ROI\n'
im = Image(fname_image)
nx, ny, nz, nt, px, py, pz, pt = im.dim
coordinates_centerline = im.getNonZeroCoordinates(sorting='z')
f = open(fname_output, "w")
sct.printv('\nWriting ROI file...', verbose)
for coord in coordinates_centerline:
coord_phys_center = im.transfo_pix2phys([[(nx - 1) / 2.0, (ny - 1) / 2.0, coord.z]])[0]
coord_phys = im.transfo_pix2phys([[coord.x, coord.y, coord.z]])[0]
f.write(ROI_TEMPLATE.format(fname_segmentation=fname_image,
creation_date=date_now.strftime("%d %B %Y %H:%M:%S.%f %Z"),
slice_num=coord.z + 1,
position_x=coord_phys_center[0] - coord_phys[0],
position_y=coord_phys_center[1] - coord_phys[1]))
f.close()
if os.path.abspath(folder_output) != os.getcwd():
sct.copy(fname_output, folder_output)
return fname_output
def ifolder2tmp(self):
self.curdir = os.getcwd()
# copy input image
if self.param.fname_im is not None:
sct.copy(self.param.fname_im, self.tmp_dir)
self.param.fname_im = ''.join(sct.extract_fname(self.param.fname_im)[1:])
else:
sct.printv('ERROR: No input image', self.param.verbose, 'error')
# copy masked image
if self.param.fname_seg is not None:
sct.copy(self.param.fname_seg, self.tmp_dir)
self.param.fname_seg = ''.join(sct.extract_fname(self.param.fname_seg)[1:])
else:
sct.printv('ERROR: No mask image', self.param.verbose, 'error')
os.chdir(self.tmp_dir) # go to tmp directory
def ifolder2tmp(self):
self.curdir = os.getcwd()
# copy input image
if self.param.fname_im is not None:
sct.copy(self.param.fname_im, self.tmp_dir)
self.param.fname_im = ''.join(extract_fname(self.param.fname_im)[1:])
else:
printv('ERROR: No input image', self.param.verbose, 'error')
# copy masked image
if self.param.fname_seg is not None:
sct.copy(self.param.fname_seg, self.tmp_dir)
self.param.fname_seg = ''.join(extract_fname(self.param.fname_seg)[1:])
else:
printv('ERROR: No mask image', self.param.verbose, 'error')
os.chdir(self.tmp_dir) # go to tmp directory
def load_manual_gmseg(list_slices_target, list_fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=False, fname_mask=None):
if isinstance(list_fname_manual_gmseg, str):
# consider fname_manual_gmseg as a list of file names to allow multiple manual GM segmentation
list_fname_manual_gmseg = [list_fname_manual_gmseg]
curdir = os.getcwd()
for fname_manual_gmseg in list_fname_manual_gmseg:
sct.copy(fname_manual_gmseg, tmp_dir)
# change fname level to only file name (path = tmp dir now)
path_gm, file_gm, ext_gm = extract_fname(fname_manual_gmseg)
fname_manual_gmseg = file_gm + ext_gm
os.chdir(tmp_dir)
im_manual_gmseg = Image(fname_manual_gmseg).change_orientation("RPI")
if fname_mask is not None:
fname_gmseg_crop = add_suffix(im_manual_gmseg.absolutepath, '_pre_crop')
crop_im = ImageCropper(input_file=im_manual_gmseg.absolutepath, output_file=fname_gmseg_crop,
mask=fname_mask)
im_manual_gmseg_crop = crop_im.crop()
im_manual_gmseg = im_manual_gmseg_crop
# assert gmseg has the right number of slices
assert im_manual_gmseg.data.shape[2] == len(list_slices_target), 'ERROR: the manual GM segmentation has not the same number of slices than the image.'
# interpolate gm to reference image
nz_gmseg, nx_gmseg, ny_gmseg, nt_gmseg, pz_gmseg, px_gmseg, py_gmseg, pt_gmseg = im_manual_gmseg.dim
list_im_gm = interpolate_im_to_ref(im_manual_gmseg, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=0)
# load gm seg in list of slices
n_poped = 0
for im_gm, slice_im in zip(list_im_gm, list_slices_target):
if im_gm.data.max() == 0 and for_model:
list_slices_target.pop(slice_im.id - n_poped)
n_poped += 1
else:
slice_im.gm_seg.append(im_gm.data)
wm_slice = (slice_im.im > 0) - im_gm.data
slice_im.wm_seg.append(wm_slice)
os.chdir(curdir)
return list_slices_target
def _update_html_assets(self, json_data):
"""Update the html file and assets"""
assets_path = os.path.join(os.path.dirname(__file__), 'assets')
dest_path = self.qc_params.root_folder
with io.open(os.path.join(assets_path, 'index.html')) as template_index:
template = Template(template_index.read())
output = template.substitute(sct_json_data=json.dumps(json_data))
io.open(os.path.join(dest_path, 'index.html'), 'w').write(output)
for path in ['css', 'js', 'imgs', 'fonts']:
src_path = os.path.join(assets_path, '_assets', path)
dest_full_path = os.path.join(dest_path, '_assets', path)
if not os.path.exists(dest_full_path):
os.makedirs(dest_full_path, exist_ok = True)
for file_ in os.listdir(src_path):
if not os.path.isfile(os.path.join(dest_full_path, file_)):
sct.copy(os.path.join(src_path, file_),
dest_full_path)
def _update_html_assets(self, json_data):
"""Update the html file and assets"""
assets_path = os.path.join(os.path.dirname(__file__), 'assets')
dest_path = self.qc_params.root_folder
with io.open(os.path.join(assets_path, 'index.html')) as template_index:
template = Template(template_index.read())
output = template.substitute(sct_json_data=json.dumps(json_data))
io.open(os.path.join(dest_path, 'index.html'), 'w').write(output)
for path in ['css', 'js', 'imgs', 'fonts']:
src_path = os.path.join(assets_path, '_assets', path)
dest_full_path = os.path.join(dest_path, '_assets', path)
if not os.path.exists(dest_full_path):
os.makedirs(dest_full_path)
for file_ in os.listdir(src_path):
if not os.path.isfile(os.path.join(dest_full_path, file_)):
sct.copy(os.path.join(src_path, file_),
dest_full_path)
def warp_label(path_label, folder_label, file_label, fname_src, fname_transfo,
path_out):
"""
Warp label files according to info_label.txt file
:param path_label:
:param folder_label:
:param file_label:
:param fname_src:
:param fname_transfo:
:param path_out:
:return:
"""
try:
# Read label file
template_label_ids, template_label_names, template_label_file, combined_labels_ids, combined_labels_names, \
combined_labels_id_groups, clusters_apriori = \
spinalcordtoolbox.metadata.read_label_file(os.path.join(path_label, folder_label), file_label)
except Exception as error:
sct.printv(
'\nWARNING: Cannot warp label ' + folder_label + ': ' + str(error),
1, 'warning')
raise
else:
# create output folder
if not os.path.exists(os.path.join(path_out, folder_label)):
os.makedirs(os.path.join(path_out, folder_label))
# Warp label
for i in range(0, len(template_label_file)):
fname_label = os.path.join(path_label, folder_label,
template_label_file[i])
# apply transfo
sct.run(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(fname_label, fname_src, fname_transfo,
os.path.join(path_out, folder_label, template_label_file[i]),
get_interp(template_label_file[i])),
is_sct_binary=True,
verbose=param.verbose)
# Copy list.txt
sct.copy(os.path.join(path_label, folder_label, param.file_info_label),
os.path.join(path_out, folder_label))
def warp_label(path_label, folder_label, file_label, fname_src, fname_transfo, path_out):
"""
Warp label files according to info_label.txt file
:param path_label:
:param folder_label:
:param file_label:
:param fname_src:
:param fname_transfo:
:param path_out:
:return:
"""
try:
# Read label file
template_label_ids, template_label_names, template_label_file, combined_labels_ids, combined_labels_names, \
combined_labels_id_groups, clusters_apriori = \
spinalcordtoolbox.metadata.read_label_file(os.path.join(path_label, folder_label), file_label)
except Exception as error:
sct.printv('\nWARNING: Cannot warp label ' + folder_label + ': ' + str(error), 1, 'warning')
raise
else:
# create output folder
if not os.path.exists(os.path.join(path_out, folder_label)):
os.makedirs(os.path.join(path_out, folder_label))
# Warp label
for i in range(0, len(template_label_file)):
fname_label = os.path.join(path_label, folder_label, template_label_file[i])
# apply transfo
sct.run('isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(fname_label,
fname_src,
fname_transfo,
os.path.join(path_out, folder_label, template_label_file[i]),
get_interp(template_label_file[i])),
is_sct_binary=True,
verbose=param.verbose)
# Copy list.txt
sct.copy(os.path.join(path_label, folder_label, param.file_info_label), os.path.join(path_out, folder_label))
def copy_data_to_tmp(self):
# copy input image
if self.param_seg.fname_im is not None:
sct.copy(self.param_seg.fname_im, self.tmp_dir)
self.param_seg.fname_im = ''.join(extract_fname(self.param_seg.fname_im)[1:])
else:
printv('ERROR: No input image', self.param.verbose, 'error')
# copy sc seg image
if self.param_seg.fname_seg is not None:
sct.copy(self.param_seg.fname_seg, self.tmp_dir)
self.param_seg.fname_seg = ''.join(extract_fname(self.param_seg.fname_seg)[1:])
else:
printv('ERROR: No SC segmentation image', self.param.verbose, 'error')
# copy level file
if self.param_seg.fname_level is not None:
sct.copy(self.param_seg.fname_level, self.tmp_dir)
self.param_seg.fname_level = ''.join(extract_fname(self.param_seg.fname_level)[1:])
if self.param_seg.fname_manual_gmseg is not None:
sct.copy(self.param_seg.fname_manual_gmseg, self.tmp_dir)
self.param_seg.fname_manual_gmseg = ''.join(extract_fname(self.param_seg.fname_manual_gmseg)[1:])
init_sct()
parser = get_parser()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_input1 = arguments.i
fname_input2 = arguments.d
verbose = arguments.v
init_sct(log_level=verbose, update=True) # Update log level
tmp_dir = sct.tmp_create(verbose=verbose) # create tmp directory
tmp_dir = os.path.abspath(tmp_dir)
# copy input files to tmp directory
# for fname in [fname_input1, fname_input2]:
sct.copy(fname_input1, tmp_dir)
sct.copy(fname_input2, tmp_dir)
fname_input1 = ''.join(sct.extract_fname(fname_input1)[1:])
fname_input2 = ''.join(sct.extract_fname(fname_input2)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir) # go to tmp directory
if arguments.bin is not None:
fname_input1_bin = sct.add_suffix(fname_input1, '_bin')
run_proc([
'sct_maths', '-i', fname_input1, '-bin', '0', '-o',
fname_input1_bin
])
fname_input1 = fname_input1_bin
fname_input2_bin = sct.add_suffix(fname_input2, '_bin')
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
resample_to = 0.1
if arguments.d is not None:
input_second_fname = arguments.d
if arguments.thinning is not None:
param.thinning = bool(arguments.thinning)
if arguments.resampling is not None:
resample_to = arguments.resampling
if arguments.o is not None:
output_fname = arguments.o
param.verbose = arguments.v
sct.init_sct(log_level=param.verbose, update=True) # Update log level
tmp_dir = sct.tmp_create()
im1_name = "im1.nii.gz"
sct.copy(input_fname, os.path.join(tmp_dir, im1_name))
if input_second_fname != '':
im2_name = 'im2.nii.gz'
sct.copy(input_second_fname, os.path.join(tmp_dir, im2_name))
else:
im2_name = None
curdir = os.getcwd()
os.chdir(tmp_dir)
# now = time.time()
input_im1 = Image(
resample_image(im1_name,
binary=True,
thr=0.5,
npx=resample_to,
def fmri_moco(param):
file_data = 'fmri'
ext_data = '.nii'
mat_final = 'mat_final/'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data + '.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), param.verbose)
# Get orientation
sct.printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
sct.printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
sct.printv(' Treated as axial')
else:
param.is_sagittal = False
sct.printv('WARNING: Orientation seems to be neither axial nor sagittal.')
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
sct.printv('For sagittal data group_size should be one for more robustness. Forcing group_size=1.', 1, 'warning')
param.group_size = 1
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data = Image(file_data + ext_data)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
im.save()
# assign an index to each volume
index_fmri = list(range(0, nt))
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup * param.group_size):((iGroup + 1) * param.group_size)])
# add the remaining images to the last fMRI group
nb_remaining = nt%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) - nb_remaining:len(index_fmri)])
# groups
for iGroup in tqdm(range(nb_groups), unit='iter', unit_scale=False, desc="Merge within groups", ascii=True, ncols=80):
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
file_data_merge_i = file_data + '_' + str(iGroup)
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3, squeeze_data=True).save(file_data_merge_i + ext_data)
file_data_mean = file_data + '_mean_' + str(iGroup)
if param.group_size == 1:
# copy to new file name instead of averaging (faster)
# note: this is a bandage. Ideally we should skip this entire for loop if g=1
sct.copy(file_data_merge_i + '.nii', file_data_mean + '.nii')
else:
# Average Images
sct.run(['sct_maths', '-i', file_data_merge_i + '.nii', '-o', file_data_mean + '.nii', '-mean', 't'], verbose=0)
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means. The output 4D volume will be used for motion correction.
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups'
im_mean_list = []
for iGroup in range(nb_groups):
im_mean_list.append(Image(file_data + '_mean_' + str(iGroup) + ext_data))
im_mean_concat = concat_data(im_mean_list, 3).save(file_data_groups_means_merge + ext_data)
# Estimate moco
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups'
param_moco.file_target = file_data + '_mean_' + param.num_target
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat = moco.moco(param_moco)
# TODO: if g=1, no need to run the block below (already applied)
if param.group_size == 1:
# if flag g=1, it means that all images have already been corrected, so we just need to rename the file
sct.mv('fmri_averaged_groups_moco.nii', 'fmri_moco.nii')
else:
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(len(group_indexes[iGroup])): # we cannot use enumerate because group_indexes has 2 dim.
# fetch all file_mat_z for given t-group
list_file_mat_z = file_mat[:, iGroup]
# loop across file_mat_z and copy to mat_final folder
for file_mat_z in list_file_mat_z:
# we want to copy 'mat_groups/mat.ZXXXXTYYYYWarp.nii.gz' --> 'mat_final/mat.ZXXXXTYYYZWarp.nii.gz'
# Notice the Y->Z in the under the T index: the idea here is to use the single matrix from each group,
# and apply it to all images belonging to the same group.
sct.copy(file_mat_z + ext_mat,
mat_final + file_mat_z[11:20] + 'T' + str(group_indexes[iGroup][data]).zfill(4) + ext_mat)
# Apply moco on all fmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = 'fmri'
param_moco.file_target = file_data + '_mean_' + str(0)
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco.header = im_fmri.header
im_fmri_moco.save()
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct_maths.main(args=['-i', 'fmri_moco.nii',
'-o', 'fmri_moco_mean.nii',
'-mean', 't',
'-v', '0'])
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 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 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):
# 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 moco(param):
# retrieve parameters
file_data = param.file_data
file_target = param.file_target
folder_mat = param.mat_moco # output folder of mat file
todo = param.todo
suffix = param.suffix
verbose = param.verbose
# other parameters
file_mask = 'mask.nii'
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' Input file ............' + file_data, param.verbose)
sct.printv(' Reference file ........' + file_target, param.verbose)
sct.printv(' Polynomial degree .....' + param.poly, param.verbose)
sct.printv(' Smoothing kernel ......' + param.smooth, param.verbose)
sct.printv(' Gradient step .........' + param.gradStep, param.verbose)
sct.printv(' Metric ................' + param.metric, param.verbose)
sct.printv(' Sampling ..............' + param.sampling, param.verbose)
sct.printv(' Todo ..................' + todo, param.verbose)
sct.printv(' Mask .................' + param.fname_mask, param.verbose)
sct.printv(' Output mat folder .....' + folder_mat, param.verbose)
# create folder for mat files
sct.create_folder(folder_mat)
# Get size of data
sct.printv('\nData dimensions:', verbose)
im_data = Image(param.file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv((' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt)), verbose)
# copy file_target to a temporary file
sct.printv('\nCopy file_target to a temporary file...', verbose)
file_target = "target.nii.gz"
convert(param.file_target, file_target)
# If scan is sagittal, split src and target along Z (slice)
if param.is_sagittal:
dim_sag = 2 # TODO: find it
# z-split data (time series)
im_z_list = split_data(im_data, dim=dim_sag, squeeze_data=False)
file_data_splitZ = []
for im_z in im_z_list:
im_z.save()
file_data_splitZ.append(im_z.absolutepath)
# z-split target
im_targetz_list = split_data(Image(file_target), dim=dim_sag, squeeze_data=False)
file_target_splitZ = []
for im_targetz in im_targetz_list:
im_targetz.save()
file_target_splitZ.append(im_targetz.absolutepath)
# z-split mask (if exists)
if not param.fname_mask == '':
im_maskz_list = split_data(Image(file_mask), dim=dim_sag, squeeze_data=False)
file_mask_splitZ = []
for im_maskz in im_maskz_list:
im_maskz.save()
file_mask_splitZ.append(im_maskz.absolutepath)
# initialize file list for output matrices
file_mat = np.empty((nz, nt), dtype=object)
# axial orientation
else:
file_data_splitZ = [file_data] # TODO: make it absolute like above
file_target_splitZ = [file_target] # TODO: make it absolute like above
# initialize file list for output matrices
file_mat = np.empty((1, nt), dtype=object)
# deal with mask
if not param.fname_mask == '':
convert(param.fname_mask, file_mask, squeeze_data=False)
im_maskz_list = [Image(file_mask)] # use a list with single element
# Loop across file list, where each file is either a 2D volume (if sagittal) or a 3D volume (otherwise)
# file_mat = tuple([[[] for i in range(nt)] for i in range(nz)])
file_data_splitZ_moco = []
sct.printv('\nRegister. Loop across Z (note: there is only one Z if orientation is axial')
for file in file_data_splitZ:
iz = file_data_splitZ.index(file)
# Split data along T dimension
# sct.printv('\nSplit data along T dimension.', verbose)
im_z = Image(file)
list_im_zt = split_data(im_z, dim=3)
file_data_splitZ_splitT = []
for im_zt in list_im_zt:
im_zt.save(verbose=0)
file_data_splitZ_splitT.append(im_zt.absolutepath)
# file_data_splitT = file_data + '_T'
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitZ_splitT_moco = []
failed_transfo = [0 for i in range(nt)]
# Motion correction: Loop across T
for indice_index in tqdm(range(nt), unit='iter', unit_scale=False,
desc="Z=" + str(iz) + "/" + str(len(file_data_splitZ)-1), ascii=True, ncols=80):
# create indices and display stuff
it = index[indice_index]
file_mat[iz][it] = os.path.join(folder_mat, "mat.Z") + str(iz).zfill(4) + 'T' + str(it).zfill(4)
file_data_splitZ_splitT_moco.append(sct.add_suffix(file_data_splitZ_splitT[it], '_moco'))
# deal with masking
if not param.fname_mask == '':
input_mask = im_maskz_list[iz]
else:
input_mask = None
# run 3D registration
failed_transfo[it] = register(param, file_data_splitZ_splitT[it], file_target_splitZ[iz], file_mat[iz][it],
file_data_splitZ_splitT_moco[it], im_mask=input_mask)
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterAvg and indice_index < 10 and failed_transfo[it] == 0 and not param.todo == 'apply':
im_targetz = Image(file_target_splitZ[iz])
data_targetz = im_targetz.data
data_mocoz = Image(file_data_splitZ_splitT_moco[it]).data
data_targetz = (data_targetz * (indice_index + 1) + data_mocoz) / (indice_index + 2)
im_targetz.data = data_targetz
im_targetz.save(verbose=0)
# Replace failed transformation with the closest good one
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [np.abs(gT[i] - fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
sct.printv(' transfo #' + str(fT[it]) + ' --> use transfo #' + str(gT[index_good]), verbose)
# copy transformation
sct.copy(file_mat[iz][gT[index_good]] + 'Warp.nii.gz', file_mat[iz][fT[it]] + 'Warp.nii.gz')
# apply transformation
sct_apply_transfo.main(args=['-i', file_data_splitZ_splitT[fT[it]],
'-d', file_target,
'-w', file_mat[iz][fT[it]] + 'Warp.nii.gz',
'-o', file_data_splitZ_splitT_moco[fT[it]],
'-x', param.interp])
else:
# exit program if no transformation exists.
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': No good transformation exist. Exit program.\n', verbose, 'error')
sys.exit(2)
# Merge data along T
file_data_splitZ_moco.append(sct.add_suffix(file, suffix))
if todo != 'estimate':
im_out = concat_data(file_data_splitZ_splitT_moco, 3)
im_out.save(file_data_splitZ_moco[iz])
# If sagittal, merge along Z
if param.is_sagittal:
im_out = concat_data(file_data_splitZ_moco, 2)
dirname, basename, ext = sct.extract_fname(file_data)
path_out = os.path.join(dirname, basename + suffix + ext)
im_out.save(path_out)
return file_mat
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 detect_centerline(image_fname,
contrast_type,
optic_models_path,
folder_output,
remove_temp_files=False,
init_option=None,
output_roi=False,
verbose=0):
"""This method will use the OptiC to detect the centerline.
:param image_fname: The input image filename.
:param init_option: Axial slice where the propagation starts.
:param contrast_type: The contrast type.
:param optic_models_path: The path with the Optic model files.
:param folder_output: The OptiC output folder.
:param remove_temp_files: Remove the temporary created files.
:param verbose: Adjusts the verbosity of the logging.
:returns: The OptiC output filename.
"""
image_input = Image(image_fname)
path_data, file_data, ext_data = sct.extract_fname(image_fname)
sct.printv('Detecting the spinal cord using OptiC', verbose=verbose)
image_input_orientation = image_input.orientation
temp_folder = sct.TempFolder()
temp_folder.copy_from(image_fname)
curdir = os.getcwd()
temp_folder.chdir()
# convert image data type to int16, as required by opencv (backend in OptiC)
image_int_filename = sct.add_suffix(file_data + ext_data, "_int16")
img = Image(image_fname)
img_int16 = img.copy()
# rescale intensity
min_out = np.iinfo('uint16').min
max_out = np.iinfo('uint16').max
min_in = np.nanmin(img.data)
max_in = np.nanmax(img.data)
data_rescaled = img.data.astype('float') * (max_out - min_out) / (max_in -
min_in)
img_int16.data = data_rescaled - (data_rescaled.min() - min_out)
# change data type
img_int16.save(image_int_filename, dtype=np.uint16)
del img, img_int16
# reorient the input image to RPI + convert to .nii
reoriented_image_filename = sct.add_suffix(image_int_filename, "_RPI")
img_filename = ''.join(sct.extract_fname(reoriented_image_filename)[:2])
reoriented_image_filename_nii = img_filename + '.nii'
cmd_reorient = 'sct_image -i "%s" -o "%s" -setorient RPI -v 0' % \
(image_int_filename, reoriented_image_filename_nii)
sct.run(cmd_reorient, verbose=0)
image_rpi_init = Image(reoriented_image_filename_nii)
nxr, nyr, nzr, ntr, pxr, pyr, pzr, ptr = image_rpi_init.dim
if init_option is not None:
if init_option > 1:
init_option /= (nzr - 1)
# call the OptiC method to generate the spinal cord centerline
optic_input = img_filename
optic_filename = img_filename + '_optic'
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_optic = 'isct_spine_detect -ctype=dpdt -lambda=1 "%s" "%s" "%s"' % \
(optic_models_path, optic_input, optic_filename)
sct.run(cmd_optic, verbose=0)
# convert .img and .hdr files to .nii.gz
optic_hdr_filename = img_filename + '_optic_ctr.hdr'
centerline_optic_RPI_filename = sct.add_suffix(file_data + ext_data,
"_centerline_optic_RPI")
img = nib.load(optic_hdr_filename)
nib.save(img, centerline_optic_RPI_filename)
# reorient the output image to initial orientation
centerline_optic_filename = sct.add_suffix(file_data + ext_data,
"_centerline_optic")
cmd_reorient = 'sct_image -i "%s" -o "%s" -setorient "%s" -v 0' % \
(centerline_optic_RPI_filename,
centerline_optic_filename,
image_input_orientation)
sct.run(cmd_reorient, verbose=0)
# copy centerline to parent folder
folder_output_from_temp = folder_output
if not os.path.isabs(folder_output):
folder_output_from_temp = os.path.join(curdir, folder_output)
sct.printv('Copy output to ' + folder_output, verbose=0)
sct.copy(centerline_optic_filename, folder_output_from_temp)
if output_roi:
fname_roi_centerline = centerline2roi(
fname_image=centerline_optic_RPI_filename,
folder_output=folder_output_from_temp,
verbose=verbose)
# Note: the .roi file is defined in RPI orientation. To be used, it must be applied on the original image with
# a RPI orientation. For this reason, this script also outputs the input image in RPI orientation
sct.copy(reoriented_image_filename_nii, folder_output_from_temp)
# return to initial folder
temp_folder.chdir_undo()
# delete temporary folder
if remove_temp_files:
temp_folder.cleanup()
return init_option, os.path.join(folder_output, centerline_optic_filename)
def register(param, file_src, file_dest, file_mat, file_out, im_mask=None):
"""
Register two images by estimating slice-wise Tx and Ty transformations, which are regularized along Z. This function
uses ANTs' isct_antsSliceRegularizedRegistration.
:param param:
:param file_src:
:param file_dest:
:param file_mat:
:param file_out:
:param im_mask: Image of mask, could be 2D or 3D
:return:
"""
# TODO: deal with mask
# initialization
failed_transfo = 0 # by default, failed matrix is 0 (i.e., no failure)
do_registration = True
# get metric radius (if MeanSquares, CC) or nb bins (if MI)
if param.metric == 'MI':
metric_radius = '16'
else:
metric_radius = '4'
file_out_concat = file_out
kw = dict()
im_data = Image(file_src) # TODO: pass argument to use antsReg instead of opening Image each time
# register file_src to file_dest
if param.todo == 'estimate' or param.todo == 'estimate_and_apply':
# If orientation is sagittal, use antsRegistration in 2D mode
# Note: the parameter --restrict-deformation is irrelevant with affine transfo
if im_data.orientation[2] in 'LR':
cmd = ['isct_antsRegistration',
'-d', '2',
'--transform', 'Affine[%s]' %param.gradStep,
'--metric', param.metric + '[' + file_dest + ',' + file_src + ',1,' + metric_radius + ',Regular,' + param.sampling + ']',
'--convergence', param.iter,
'--shrink-factors', '1',
'--smoothing-sigmas', param.smooth,
'--verbose', '1',
'--output', '[' + file_mat + ',' + file_out_concat + ']']
cmd += sct.get_interpolation('isct_antsRegistration', param.interp)
if im_mask is not None:
# if user specified a mask, make sure there are non-null voxels in the image before running the registration
if np.count_nonzero(im_mask.data):
cmd += ['--masks', im_mask.absolutepath]
else:
# Mask only contains zeros. Copying the image instead of estimating registration.
sct.copy(file_src, file_out_concat, verbose=0)
do_registration = False
# TODO: create affine mat file with identity, in case used by -g 2
# 3D mode
else:
cmd = ['isct_antsSliceRegularizedRegistration',
'--polydegree', param.poly,
'--transform', 'Translation[%s]' %param.gradStep,
'--metric', param.metric + '[' + file_dest + ',' + file_src + ',1,' + metric_radius + ',Regular,' + param.sampling + ']',
'--iterations', param.iter,
'--shrinkFactors', '1',
'--smoothingSigmas', param.smooth,
'--verbose', '1',
'--output', '[' + file_mat + ',' + file_out_concat + ']']
cmd += sct.get_interpolation('isct_antsSliceRegularizedRegistration', param.interp)
if im_mask is not None:
cmd += ['--mask', im_mask.absolutepath]
# run command
if do_registration:
kw.update(dict(is_sct_binary=True))
env = dict()
env.update(os.environ)
env = kw.get("env", env)
# reducing the number of CPU used for moco (see issue #201)
env["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "1"
status, output = sct.run(cmd, verbose=0, **kw)
elif param.todo == 'apply':
sct_apply_transfo.main(args=['-i', file_src,
'-d', file_dest,
'-w', file_mat + 'Warp.nii.gz',
'-o', file_out_concat,
'-x', param.interp,
'-v', '0'])
# check if output file exists
if not os.path.isfile(file_out_concat):
# sct.printv(output, verbose, 'error')
sct.printv('WARNING in ' + os.path.basename(__file__) + ': No output. Maybe related to improper calculation of '
'mutual information. Either the mask you provided is '
'too small, or the subject moved a lot. If you see too '
'many messages like this try with a bigger mask. '
'Using previous transformation for this volume (if it'
'exists).', param.verbose, 'warning')
failed_transfo = 1
# TODO: if sagittal, copy header (because ANTs screws it) and add singleton in 3rd dimension (for z-concatenation)
if im_data.orientation[2] in 'LR' and do_registration:
im_out = Image(file_out_concat)
im_out.header = im_data.header
im_out.data = np.expand_dims(im_out.data, 2)
im_out.save(file_out, verbose=0)
# return status of failure
return failed_transfo
def fmri_moco(param):
file_data = "fmri.nii"
mat_final = 'mat_final/'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(param.fname_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), param.verbose)
# Get orientation
sct.printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
sct.printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
sct.printv(' Treated as axial')
else:
param.is_sagittal = False
sct.printv('WARNING: Orientation seems to be neither axial nor sagittal.')
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
sct.printv('For sagittal data group_size should be one for more robustness. Forcing group_size=1.', 1, 'warning')
param.group_size = 1
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
# Make further steps slurp the data to avoid too many open files (#2149)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# assign an index to each volume
index_fmri = list(range(0, nt))
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup * param.group_size):((iGroup + 1) * param.group_size)])
# add the remaining images to the last fMRI group
nb_remaining = nt%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) - nb_remaining:len(index_fmri)])
# groups
for iGroup in tqdm(range(nb_groups), unit='iter', unit_scale=False, desc="Merge within groups", ascii=True, ncols=80):
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
file_data_merge_i = sct.add_suffix(file_data, '_' + str(iGroup))
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3, squeeze_data=True).save(file_data_merge_i)
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
if param.group_size == 1:
# copy to new file name instead of averaging (faster)
# note: this is a bandage. Ideally we should skip this entire for loop if g=1
convert(file_data_merge_i, file_data_mean)
else:
# Average Images
sct.run(['sct_maths', '-i', file_data_merge_i, '-o', file_data_mean, '-mean', 't'], verbose=0)
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means. The output 4D volume will be used for motion correction.
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups.nii'
im_mean_list = []
for iGroup in range(nb_groups):
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
im_mean_list.append(Image(file_data_mean))
im_mean_concat = concat_data(im_mean_list, 3).save(file_data_groups_means_merge)
# Estimate moco
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups.nii'
param_moco.file_target = sct.add_suffix(file_data, '_mean_' + param.num_target)
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz" # #2149
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat = moco.moco(param_moco)
# TODO: if g=1, no need to run the block below (already applied)
if param.group_size == 1:
# if flag g=1, it means that all images have already been corrected, so we just need to rename the file
sct.mv('fmri_averaged_groups_moco.nii', 'fmri_moco.nii')
else:
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(len(group_indexes[iGroup])): # we cannot use enumerate because group_indexes has 2 dim.
# fetch all file_mat_z for given t-group
list_file_mat_z = file_mat[:, iGroup]
# loop across file_mat_z and copy to mat_final folder
for file_mat_z in list_file_mat_z:
# we want to copy 'mat_groups/mat.ZXXXXTYYYYWarp.nii.gz' --> 'mat_final/mat.ZXXXXTYYYZWarp.nii.gz'
# Notice the Y->Z in the under the T index: the idea here is to use the single matrix from each group,
# and apply it to all images belonging to the same group.
sct.copy(file_mat_z + ext_mat,
mat_final + file_mat_z[11:20] + 'T' + str(group_indexes[iGroup][data]).zfill(4) + ext_mat)
# Apply moco on all fmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = 'fmri.nii'
param_moco.file_target = sct.add_suffix(file_data, '_mean_' + str(0))
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz"
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco.header = im_fmri.header
im_fmri_moco.save()
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct_maths.main(args=['-i', 'fmri_moco.nii',
'-o', 'fmri_moco_mean.nii',
'-mean', 't',
'-v', '0'])
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 fmri_moco(param):
file_data = "fmri.nii"
mat_final = 'mat_final/'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(param.fname_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
# Get orientation
sct.printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
sct.printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
sct.printv(' Treated as axial')
else:
param.is_sagittal = False
sct.printv(
'WARNING: Orientation seems to be neither axial nor sagittal.')
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
sct.printv(
'For sagittal data group_size should be one for more robustness. Forcing group_size=1.',
1, 'warning')
param.group_size = 1
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
# Make further steps slurp the data to avoid too many open files (#2149)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# assign an index to each volume
index_fmri = list(range(0, nt))
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last fMRI group
nb_remaining = nt % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) -
nb_remaining:len(index_fmri)])
# groups
for iGroup in tqdm(range(nb_groups),
unit='iter',
unit_scale=False,
desc="Merge within groups",
ascii=True,
ncols=80):
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
file_data_merge_i = sct.add_suffix(file_data, '_' + str(iGroup))
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3,
squeeze_data=True).save(file_data_merge_i)
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
if param.group_size == 1:
# copy to new file name instead of averaging (faster)
# note: this is a bandage. Ideally we should skip this entire for loop if g=1
convert(file_data_merge_i, file_data_mean)
else:
# Average Images
sct.run([
'sct_maths', '-i', file_data_merge_i, '-o', file_data_mean,
'-mean', 't'
],
verbose=0)
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means. The output 4D volume will be used for motion correction.
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups.nii'
im_mean_list = []
for iGroup in range(nb_groups):
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
im_mean_list.append(Image(file_data_mean))
im_mean_concat = concat_data(im_mean_list,
3).save(file_data_groups_means_merge)
# Estimate moco
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups.nii'
param_moco.file_target = sct.add_suffix(file_data,
'_mean_' + param.num_target)
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz" # #2149
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat = moco.moco(param_moco)
# TODO: if g=1, no need to run the block below (already applied)
if param.group_size == 1:
# if flag g=1, it means that all images have already been corrected, so we just need to rename the file
sct.mv('fmri_averaged_groups_moco.nii', 'fmri_moco.nii')
else:
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(
len(group_indexes[iGroup])
): # we cannot use enumerate because group_indexes has 2 dim.
# fetch all file_mat_z for given t-group
list_file_mat_z = file_mat[:, iGroup]
# loop across file_mat_z and copy to mat_final folder
for file_mat_z in list_file_mat_z:
# we want to copy 'mat_groups/mat.ZXXXXTYYYYWarp.nii.gz' --> 'mat_final/mat.ZXXXXTYYYZWarp.nii.gz'
# Notice the Y->Z in the under the T index: the idea here is to use the single matrix from each group,
# and apply it to all images belonging to the same group.
sct.copy(
file_mat_z + ext_mat,
mat_final + file_mat_z[11:20] + 'T' +
str(group_indexes[iGroup][data]).zfill(4) + ext_mat)
# Apply moco on all fmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = 'fmri.nii'
param_moco.file_target = sct.add_suffix(file_data, '_mean_' + str(0))
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz"
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
file_mat = moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco.header = im_fmri.header
im_fmri_moco.save()
# Extract and output the motion parameters
if param.output_motion_param:
from sct_image import multicomponent_split
import csv
#files_warp = []
files_warp_X, files_warp_Y = [], []
moco_param = []
for fname_warp in file_mat[0]:
# Cropping the image to keep only one voxel in the XY plane
im_warp = Image(fname_warp + ext_mat)
im_warp.data = np.expand_dims(np.expand_dims(
im_warp.data[0, 0, :, :, :], axis=0),
axis=0)
# These three lines allow to generate one file instead of two, containing X, Y and Z moco parameters
#fname_warp_crop = fname_warp + '_crop_' + ext_mat
#files_warp.append(fname_warp_crop)
#im_warp.save(fname_warp_crop)
# Separating the three components and saving X and Y only (Z is equal to 0 by default).
im_warp_XYZ = multicomponent_split(im_warp)
fname_warp_crop_X = fname_warp + '_crop_X_' + ext_mat
im_warp_XYZ[0].save(fname_warp_crop_X)
files_warp_X.append(fname_warp_crop_X)
fname_warp_crop_Y = fname_warp + '_crop_Y_' + ext_mat
im_warp_XYZ[1].save(fname_warp_crop_Y)
files_warp_Y.append(fname_warp_crop_Y)
# Calculating the slice-wise average moco estimate to provide a QC file
moco_param.append([
np.mean(np.ravel(im_warp_XYZ[0].data)),
np.mean(np.ravel(im_warp_XYZ[1].data))
])
# These two lines allow to generate one file instead of two, containing X, Y and Z moco parameters
#im_warp_concat = concat_data(files_warp, dim=3)
#im_warp_concat.save('fmri_moco_params.nii')
# Concatenating the moco parameters into a time series for X and Y components.
im_warp_concat = concat_data(files_warp_X, dim=3)
im_warp_concat.save('fmri_moco_params_X.nii')
im_warp_concat = concat_data(files_warp_Y, dim=3)
im_warp_concat.save('fmri_moco_params_Y.nii')
# Writing a TSV file with the slicewise average estimate of the moco parameters, as it is a useful QC file.
with open('fmri_moco_params.tsv', 'wt') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
tsv_writer.writerow(['X', 'Y'])
for mocop in moco_param:
tsv_writer.writerow([mocop[0], mocop[1]])
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct_maths.main(args=[
'-i', 'fmri_moco.nii', '-o', 'fmri_moco_mean.nii', '-mean', 't', '-v',
'0'
])
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 propseg(img_input, options_dict):
"""
:param img_input: source image, to be segmented
:param options_dict: arguments as dictionary
:return: segmented Image
"""
arguments = options_dict
fname_input_data = img_input.absolutepath
fname_data = os.path.abspath(fname_input_data)
contrast_type = arguments["-c"]
contrast_type_conversion = {'t1': 't1', 't2': 't2', 't2s': 't2', 'dwi': 't1'}
contrast_type_propseg = contrast_type_conversion[contrast_type]
# Starting building the command
cmd = ['isct_propseg', '-t', contrast_type_propseg]
if "-ofolder" in arguments:
folder_output = arguments["-ofolder"]
else:
folder_output = './'
cmd += ['-o', folder_output]
if not os.path.isdir(folder_output) and os.path.exists(folder_output):
logger.error("output directory %s is not a valid directory" % folder_output)
if not os.path.exists(folder_output):
os.makedirs(folder_output)
if "-down" in arguments:
cmd += ["-down", str(arguments["-down"])]
if "-up" in arguments:
cmd += ["-up", str(arguments["-up"])]
remove_temp_files = 1
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
# Update for propseg binary
if verbose > 0:
cmd += ["-verbose"]
# Output options
if "-mesh" in arguments:
cmd += ["-mesh"]
if "-centerline-binary" in arguments:
cmd += ["-centerline-binary"]
if "-CSF" in arguments:
cmd += ["-CSF"]
if "-centerline-coord" in arguments:
cmd += ["-centerline-coord"]
if "-cross" in arguments:
cmd += ["-cross"]
if "-init-tube" in arguments:
cmd += ["-init-tube"]
if "-low-resolution-mesh" in arguments:
cmd += ["-low-resolution-mesh"]
if "-detect-nii" in arguments:
cmd += ["-detect-nii"]
if "-detect-png" in arguments:
cmd += ["-detect-png"]
# Helping options
use_viewer = None
use_optic = True # enabled by default
init_option = None
rescale_header = arguments["-rescale"]
if "-init" in arguments:
init_option = float(arguments["-init"])
if init_option < 0:
sct.printv('Command-line usage error: ' + str(init_option) + " is not a valid value for '-init'", 1, 'error')
sys.exit(1)
if "-init-centerline" in arguments:
if str(arguments["-init-centerline"]) == "viewer":
use_viewer = "centerline"
elif str(arguments["-init-centerline"]) == "hough":
use_optic = False
else:
if rescale_header is not 1:
fname_labels_viewer = func_rescale_header(str(arguments["-init-centerline"]), rescale_header, verbose=verbose)
else:
fname_labels_viewer = str(arguments["-init-centerline"])
cmd += ["-init-centerline", fname_labels_viewer]
use_optic = False
if "-init-mask" in arguments:
if str(arguments["-init-mask"]) == "viewer":
use_viewer = "mask"
else:
if rescale_header is not 1:
fname_labels_viewer = func_rescale_header(str(arguments["-init-mask"]), rescale_header)
else:
fname_labels_viewer = str(arguments["-init-mask"])
cmd += ["-init-mask", fname_labels_viewer]
use_optic = False
if "-mask-correction" in arguments:
cmd += ["-mask-correction", str(arguments["-mask-correction"])]
if "-radius" in arguments:
cmd += ["-radius", str(arguments["-radius"])]
if "-detect-n" in arguments:
cmd += ["-detect-n", str(arguments["-detect-n"])]
if "-detect-gap" in arguments:
cmd += ["-detect-gap", str(arguments["-detect-gap"])]
if "-init-validation" in arguments:
cmd += ["-init-validation"]
if "-nbiter" in arguments:
cmd += ["-nbiter", str(arguments["-nbiter"])]
if "-max-area" in arguments:
cmd += ["-max-area", str(arguments["-max-area"])]
if "-max-deformation" in arguments:
cmd += ["-max-deformation", str(arguments["-max-deformation"])]
if "-min-contrast" in arguments:
cmd += ["-min-contrast", str(arguments["-min-contrast"])]
if "-d" in arguments:
cmd += ["-d", str(arguments["-d"])]
if "-distance-search" in arguments:
cmd += ["-dsearch", str(arguments["-distance-search"])]
if "-alpha" in arguments:
cmd += ["-alpha", str(arguments["-alpha"])]
# check if input image is in 3D. Otherwise itk image reader will cut the 4D image in 3D volumes and only take the first one.
image_input = Image(fname_data)
image_input_rpi = image_input.copy().change_orientation('RPI')
nx, ny, nz, nt, px, py, pz, pt = image_input_rpi.dim
if nt > 1:
sct.printv('ERROR: your input image needs to be 3D in order to be segmented.', 1, 'error')
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_tmp = sct.tmp_create(basename="label_vertebrae", verbose=verbose)
# rescale header (see issue #1406)
if rescale_header is not 1:
fname_data_propseg = func_rescale_header(fname_data, rescale_header)
else:
fname_data_propseg = fname_data
# add to command
cmd += ['-i', fname_data_propseg]
# if centerline or mask is asked using viewer
if use_viewer:
from spinalcordtoolbox.gui.base import AnatomicalParams
from spinalcordtoolbox.gui.centerline import launch_centerline_dialog
params = AnatomicalParams()
if use_viewer == 'mask':
params.num_points = 3
params.interval_in_mm = 15 # superior-inferior interval between two consecutive labels
params.starting_slice = 'midfovminusinterval'
if use_viewer == 'centerline':
# setting maximum number of points to a reasonable value
params.num_points = 20
params.interval_in_mm = 30
params.starting_slice = 'top'
im_data = Image(fname_data_propseg)
im_mask_viewer = msct_image.zeros_like(im_data)
# im_mask_viewer.absolutepath = sct.add_suffix(fname_data_propseg, '_labels_viewer')
controller = launch_centerline_dialog(im_data, im_mask_viewer, params)
fname_labels_viewer = sct.add_suffix(fname_data_propseg, '_labels_viewer')
if not controller.saved:
sct.printv('The viewer has been closed before entering all manual points. Please try again.', 1, 'error')
sys.exit(1)
# save labels
controller.as_niftii(fname_labels_viewer)
# add mask filename to parameters string
if use_viewer == "centerline":
cmd += ["-init-centerline", fname_labels_viewer]
elif use_viewer == "mask":
cmd += ["-init-mask", fname_labels_viewer]
# If using OptiC
elif use_optic:
image_centerline = optic.detect_centerline(image_input, contrast_type, verbose)
fname_centerline_optic = os.path.join(path_tmp, 'centerline_optic.nii.gz')
image_centerline.save(fname_centerline_optic)
cmd += ["-init-centerline", fname_centerline_optic]
if init_option is not None:
if init_option > 1:
init_option /= (nz - 1)
cmd += ['-init', str(init_option)]
# enabling centerline extraction by default (needed by check_and_correct_segmentation() )
cmd += ['-centerline-binary']
# run propseg
status, output = sct.run(cmd, verbose, raise_exception=False, is_sct_binary=True)
# check status is not 0
if not status == 0:
sct.printv('Automatic cord detection failed. Please initialize using -init-centerline or -init-mask (see help)',
1, 'error')
sys.exit(1)
# build output filename
fname_seg = os.path.join(folder_output, os.path.basename(sct.add_suffix(fname_data, "_seg")))
fname_centerline = os.path.join(folder_output, os.path.basename(sct.add_suffix(fname_data, "_centerline")))
# in case header was rescaled, we need to update the output file names by removing the "_rescaled"
if rescale_header is not 1:
sct.mv(os.path.join(folder_output, sct.add_suffix(os.path.basename(fname_data_propseg), "_seg")),
fname_seg)
sct.mv(os.path.join(folder_output, sct.add_suffix(os.path.basename(fname_data_propseg), "_centerline")),
fname_centerline)
# if user was used, copy the labelled points to the output folder (they will then be scaled back)
if use_viewer:
fname_labels_viewer_new = os.path.join(folder_output, os.path.basename(sct.add_suffix(fname_data,
"_labels_viewer")))
sct.copy(fname_labels_viewer, fname_labels_viewer_new)
# update variable (used later)
fname_labels_viewer = fname_labels_viewer_new
# check consistency of segmentation
if arguments["-correct-seg"] == "1":
check_and_correct_segmentation(fname_seg, fname_centerline, folder_output=folder_output, threshold_distance=3.0,
remove_temp_files=remove_temp_files, verbose=verbose)
# copy header from input to segmentation to make sure qform is the same
sct.printv("Copy header input --> output(s) to make sure qform is the same.", verbose)
list_fname = [fname_seg, fname_centerline]
if use_viewer:
list_fname.append(fname_labels_viewer)
for fname in list_fname:
im = Image(fname)
im.header = image_input.header
im.save(dtype='int8') # they are all binary masks hence fine to save as int8
return Image(fname_seg)
def 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 compute_ratio(self):
type_ratio = self.param_seg.ratio
tmp_dir_ratio = tmp_create(basename="compute_ratio")
curdir = os.getcwd()
os.chdir(tmp_dir_ratio)
fname_gmseg = self.im_res_gmseg.absolutepath
fname_wmseg = self.im_res_wmseg.absolutepath
if self.im_res_gmseg.orientation != "RPI":
fname_gmseg = self.im_res_gmseg.change_orientation(
self.im_res_gmseg, 'RPI',
generate_path=True).save().absolutepath
fname_wmseg = self.im_res_wmseg.change_orientation(
self.im_res_wmseg, 'RPI',
generate_path=True).save().absolutepath
sct_process_segmentation.main([
'-i', fname_gmseg, '-p', 'csa', '-ofolder', 'gm_csa', '-no-angle',
'1'
])
sct_process_segmentation.main([
'-i', fname_wmseg, '-p', 'csa', '-ofolder', 'wm_csa', '-no-angle',
'1'
])
gm_csa = open(os.path.join('gm_csa', 'csa_per_slice.txt'), 'r')
wm_csa = open(os.path.join('wm_csa', 'csa_per_slice.txt'), 'r')
gm_csa_lines = gm_csa.readlines()
wm_csa_lines = wm_csa.readlines()
gm_csa.close()
wm_csa.close()
fname_ratio = 'ratio_by_' + type_ratio + '.txt'
file_ratio = open(fname_ratio, 'w')
file_ratio.write(type_ratio + ', ratio GM/WM CSA\n')
csa_gm_wm_by_level = {
0: [],
1: [],
2: [],
3: [],
4: [],
5: [],
6: [],
7: [],
8: [],
9: [],
10: [],
11: [],
12: [],
13: [],
14: [],
15: [],
16: [],
17: [],
18: [],
19: [],
20: [],
21: [],
22: [],
23: [],
24: []
}
for gm_line, wm_line in zip(gm_csa_lines[1:], wm_csa_lines[1:]):
i, gm_area, gm_angle = gm_line.split(',')
j, wm_area, wm_angle = wm_line.split(',')
assert i == j
if type_ratio == 'level':
level_slice = int(self.target_im[int(i)].level)
csa_gm_wm_by_level[level_slice].append(
(float(gm_area), float(wm_area)))
else:
file_ratio.write(i + ', ' +
str(float(gm_area) / float(wm_area)) + '\n')
if type_ratio == 'level':
for l, gm_wm_list in sorted(csa_gm_wm_by_level.items()):
if str(gm_wm_list) != '[]':
csa_gm_list = []
csa_wm_list = []
for gm, wm in gm_wm_list:
csa_gm_list.append(gm)
csa_wm_list.append(wm)
csa_gm = np.mean(csa_gm_list)
csa_wm = np.mean(csa_wm_list)
file_ratio.write(
str(l) + ', ' + str(csa_gm / csa_wm) + '\n')
file_ratio.close()
sct.copy(fname_ratio,
os.path.join(self.param_seg.path_results, fname_ratio))
os.chdir(curdir)
def dmri_moco(param):
file_data = 'dmri.nii'
file_data_dirname, file_data_basename, file_data_ext = sct.extract_fname(
file_data)
file_b0 = 'b0.nii'
file_dwi = 'dwi.nii'
ext_data = '.nii.gz' # workaround "too many open files" by slurping the data
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups.nii'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
param.verbose)
# Identify b=0 and DWI images
index_b0, index_dwi, nb_b0, nb_dwi = sct_dmri_separate_b0_and_dwi.identify_b0(
'bvecs.txt', param.fname_bvals, param.bval_min, param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) + ': Size of data (' +
str(nt) + ') and size of bvecs (' + str(nb_b0 + nb_dwi) +
') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0_out = concat_data(im_b0_list, 3).save(file_b0)
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = sct.add_suffix(file_b0, '_mean')
sct.run(['sct_maths', '-i', file_b0, '-o', file_b0_mean, '-mean', 't'],
param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi) -
nb_remaining:len(index_dwi)])
file_dwi_dirname, file_dwi_basename, file_dwi_ext = sct.extract_fname(
file_dwi)
# DWI groups
file_dwi_mean = []
for iGroup in tqdm(range(nb_groups),
unit='iter',
unit_scale=False,
desc="Merge within groups",
ascii=True,
ncols=80):
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
file_dwi_merge_i = os.path.join(
file_dwi_dirname, file_dwi_basename + '_' + str(iGroup) + ext_data)
im_dwi_list = []
for it in range(nb_dwi_i):
im_dwi_list.append(im_data_split_list[index_dwi_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3).save(file_dwi_merge_i)
# Average DW Images
file_dwi_mean_i = os.path.join(
file_dwi_dirname,
file_dwi_basename + '_mean_' + str(iGroup) + ext_data)
file_dwi_mean.append(file_dwi_mean_i)
sct.run([
"sct_maths", "-i", file_dwi_merge_i, "-o", file_dwi_mean[iGroup],
"-mean", "t"
], 0)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(file_dwi_mean[iGroup])
im_dw_out = concat_data(im_dw_list, 3).save(file_dwi_group)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
sct.run([
"sct_maths", "-i", file_dwi_group, "-o",
file_dwi_group + '_mean' + ext_data, "-mean", "t"
], param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...',
param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group + '_seg.nii'
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'b0.nii'
# identify target image
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = os.path.join(
file_data_dirname, file_data_basename + '_T' +
str(index_b0[index_dwi[0] - 1]).zfill(4) + ext_data)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = os.path.join(
file_data_dirname,
file_data_basename + '_T' + str(index_b0[0]).zfill(4) + ext_data)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
file_mat_b0 = moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = file_dwi_mean[
0] # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
file_mat_dwi = moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
# TODO: use file_mat_b0 and file_mat_dwi instead of the hardcoding below
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.copy(
'mat_b0groups/' + 'mat.Z0000T' + str(it).zfill(4) + ext_mat,
mat_final + 'mat.Z0000T' + str(index_b0[it]).zfill(4) + ext_mat)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(
len(group_indexes[iGroup])
): # we cannot use enumerate because group_indexes has 2 dim.
sct.copy(
'mat_dwigroups/' + 'mat.Z0000T' + str(iGroup).zfill(4) +
ext_mat, mat_final + 'mat.Z0000T' +
str(group_indexes[iGroup][dwi]).zfill(4) + ext_mat)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0),
param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_data
param_moco.file_target = os.path.join(
file_dwi_dirname, file_dwi_basename + '_mean_' + str(0) +
ext_data) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_dmri = Image(file_data)
fname_data_moco = os.path.join(file_data_dirname,
file_data_basename + param.suffix + '.nii')
im_dmri_moco = Image(fname_data_moco)
im_dmri_moco.header = im_dmri.header
im_dmri_moco.save()
return os.path.abspath(fname_data_moco)
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)
resample_to = 0.1
if "-d" in arguments:
input_second_fname = arguments["-d"]
if "-thinning" in arguments:
param.thinning = bool(int(arguments["-thinning"]))
if "-resampling" in arguments:
resample_to = arguments["-resampling"]
if "-o" in arguments:
output_fname = arguments["-o"]
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
tmp_dir = sct.tmp_create()
im1_name = "im1.nii.gz"
sct.copy(input_fname, os.path.join(tmp_dir, im1_name))
if input_second_fname != '':
im2_name = 'im2.nii.gz'
sct.copy(input_second_fname, os.path.join(tmp_dir, im2_name))
else:
im2_name = None
curdir = os.getcwd()
os.chdir(tmp_dir)
# now = time.time()
input_im1 = Image(resample_image(im1_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to))
input_im1.absolutepath = os.path.basename(input_fname)
if im2_name is not None:
input_im2 = Image(resample_image(im2_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to))
input_im2.absolutepath = os.path.basename(input_second_fname)
def spline(folder_mat, nt, nz, verbose, index_b0 = [], graph=0):
sct.printv('\n\n\n------------------------------------------------------------------------------', verbose)
sct.printv('Spline Regularization along T: Smoothing Patient Motion...', verbose)
file_mat = [[[] for i in range(nz)] for i in range(nt)]
for it in range(nt):
for iz in range(nz):
file_mat[it][iz] = os.path.join(folder_mat, "mat.T") + str(it) + '_Z' + str(iz) + '.txt'
# Copying the existing Matrices to another folder
old_mat = os.path.join(folder_mat, "old")
if not os.path.exists(old_mat):
os.makedirs(old_mat)
# TODO
for mat in glob.glob(os.path.join(folder_mat, '*.txt')):
sct.copy(mat, old_mat)
sct.printv('\nloading matrices...', verbose)
X = [[[] for i in range(nt)] for i in range(nz)]
Y = [[[] for i in range(nt)] for i in range(nz)]
X_smooth = [[[] for i in range(nt)] for i in range(nz)]
Y_smooth = [[[] for i in range(nt)] for i in range(nz)]
for iz in range(nz):
for it in range(nt):
file = open(file_mat[it][iz])
Matrix = np.loadtxt(file)
file.close()
X[iz][it] = Matrix[0, 3]
Y[iz][it] = Matrix[1, 3]
# Generate motion splines
sct.printv('\nGenerate motion splines...', verbose)
T = np.arange(nt)
if graph:
import pylab as pl
for iz in range(nz):
spline = scipy.interpolate.UnivariateSpline(T, X[iz][:], w=None, bbox=[None, None], k=3, s=None)
X_smooth[iz][:] = spline(T)
if graph:
pl.plot(T, X_smooth[iz][:], label='spline_smoothing')
pl.plot(T, X[iz][:], marker='*', linestyle='None', label='original_val')
if len(index_b0) != 0:
T_b0 = [T[i_b0] for i_b0 in index_b0]
X_b0 = [X[iz][i_b0] for i_b0 in index_b0]
pl.plot(T_b0, X_b0, marker='D', linestyle='None', color='k', label='b=0')
pl.title('X')
pl.grid()
pl.legend()
pl.show()
spline = scipy.interpolate.UnivariateSpline(T, Y[iz][:], w=None, bbox=[None, None], k=3, s=None)
Y_smooth[iz][:] = spline(T)
if graph:
pl.plot(T, Y_smooth[iz][:], label='spline_smoothing')
pl.plot(T, Y[iz][:], marker='*', linestyle='None', label='original_val')
if len(index_b0) != 0:
T_b0 = [T[i_b0] for i_b0 in index_b0]
Y_b0 = [Y[iz][i_b0] for i_b0 in index_b0]
pl.plot(T_b0, Y_b0, marker='D', linestyle='None', color='k', label='b=0')
pl.title('Y')
pl.grid()
pl.legend()
pl.show()
# Storing the final Matrices
sct.printv('\nStoring the final Matrices...', verbose)
for iz in range(nz):
for it in range(nt):
file = open(file_mat[it][iz])
Matrix = np.loadtxt(file)
file.close()
Matrix[0, 3] = X_smooth[iz][it]
Matrix[1, 3] = Y_smooth[iz][it]
file = open(file_mat[it][iz], 'w')
np.savetxt(file_mat[it][iz], Matrix, fmt="%s", delimiter=' ', newline='\n')
file.close()
sct.printv('\n...Done. Patient motion has been smoothed', verbose)
sct.printv('------------------------------------------------------------------------------\n', verbose)
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 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 main():
# Parameters
bootstrap_iter = 200
folder_atlas = '../create_atlas/final_results/' # path to atlas. add / at the end
folder_cropped_atlas = "cropped_atlas/"
crop = 0 # crop atlas, default=1. Only need to do it once (saves time).
zcrop_ind = [10, 110, 210, 310, 410]
mask_folder = ['manual_masks/charles/', 'manual_masks/julien/', 'manual_masks/tanguy/', 'manual_masks/simon/'] # folder of manual masks
std_noise_list = [0, 5, 10, 20] # standard deviation of the noise added to the generated phantom
fixed_noise = 10 # in percent
range_tract_list = [0, 5, 10, 20] # in percent
fixed_range = 10 # in percent
val_csf_list = [5, 10, 50, 100] # in percent of white matter
val_csf_fixed = 50
list_methods = ['ml', 'map', 'wa', 'wath', 'bin', 'man0', 'man1', 'man2', 'man3']
param_map_list = ['0,20', '5,20', '10,20', '15,20', '20,20', '25,20', '30,20', '20,0', '20,5', '20,10', '20,15', '20,20', '20,25', '20,30']
results_folder = 'results/' # add / at the end
# Crop the atlas
if crop == 1:
create_folder(folder_cropped_atlas, 1)
crop_atlas(folder_atlas, folder_cropped_atlas, zcrop_ind)
# Copy the info_label.txt file in the cropped atlas' folder. This file needs to be there in order for the sct_extract_metric code to work
sct.copy(os.path.join(folder_atlas, 'info_label.txt'), folder_cropped_atlas)
# create output folder
create_folder(results_folder, 1)
# loop across noise levels
range_tract = fixed_range
val_csf = val_csf_fixed
for std_noise in std_noise_list:
results_file = 'results_noise'+str(std_noise)+'_range'+str(range_tract)+'_csf'+str(val_csf)
validate_atlas(folder_cropped_atlas, bootstrap_iter, std_noise, range_tract, val_csf, results_folder+'noise/', results_file, mask_folder, list_methods)
# loop across tract ranges
std_noise = fixed_noise
val_csf = val_csf_fixed
for range_tract in range_tract_list:
results_file = 'results_noise'+str(std_noise)+'_range'+str(range_tract)+'_csf'+str(val_csf)
validate_atlas(folder_cropped_atlas, bootstrap_iter, std_noise, range_tract, val_csf, results_folder+'tracts/', results_file, mask_folder, list_methods)
# loop across CSF value
std_noise = fixed_noise
range_tract = fixed_range
for val_csf in val_csf_list:
results_file = 'results_noise'+str(std_noise)+'_range'+str(range_tract)+'_csf'+str(val_csf)
validate_atlas(folder_cropped_atlas, bootstrap_iter, std_noise, range_tract, val_csf, results_folder+'csf/', results_file, mask_folder, list_methods)
# bin vs manual
std_noise = fixed_noise
range_tract = fixed_range
val_csf = val_csf_fixed
results_file = 'results_noise'+str(std_noise)+'_range'+str(range_tract)+'_csf'+str(val_csf)
validate_atlas(folder_cropped_atlas, bootstrap_iter, std_noise, range_tract, val_csf, results_folder+'manual_mask/', results_file, mask_folder, ['bin', 'man0', 'man1', 'man2', 'man3'], 0, '20,20', ['2', '17', '0,1,15,16'])
# loop across params for MAP estimation
std_noise = fixed_noise
range_tract = fixed_range
val_csf = val_csf_fixed
for param_map in param_map_list:
results_file = 'results_map'+str(param_map)
validate_atlas(folder_cropped_atlas, bootstrap_iter, std_noise, range_tract, val_csf, results_folder+'map/', results_file, mask_folder, ['map'], 1, param_map)
def deep_segmentation_MSlesion(fname_image, contrast_type, output_folder, ctr_algo='svm', ctr_file=None, brain_bool=True, remove_temp_files=1, verbose=1):
"""Pipeline."""
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
# create temporary folder with intermediate results
sct.log.info("\nCreating temporary folder...")
file_fname = os.path.basename(fname_image)
tmp_folder = sct.TempFolder()
tmp_folder_path = tmp_folder.get_path()
fname_image_tmp = tmp_folder.copy_from(fname_image)
if ctr_algo == 'manual': # if the ctr_file is provided
tmp_folder.copy_from(ctr_file)
file_ctr = os.path.basename(ctr_file)
else:
file_ctr = None
tmp_folder.chdir()
# orientation of the image, should be RPI
sct.log.info("\nReorient the image to RPI, if necessary...")
fname_orient = sct.add_suffix(file_fname, '_RPI')
im_2orient = Image(file_fname)
original_orientation = im_2orient.orientation
if original_orientation != 'RPI':
im_orient = msct_image.change_orientation(im_2orient, 'RPI').save(fname_orient)
else:
im_orient = im_2orient
sct.copy(fname_image_tmp, fname_orient)
input_resolution = im_orient.dim[4:7]
del im_2orient, im_orient
# find the spinal cord centerline - execute OptiC binary
sct.log.info("\nFinding the spinal cord centerline...")
contrast_type_ctr = contrast_type.split('_')[0]
fname_res, centerline_filename = find_centerline(algo=ctr_algo,
image_fname=fname_orient,
path_sct=path_sct,
contrast_type=contrast_type_ctr,
brain_bool=brain_bool,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
centerline_fname=file_ctr)
im_nii, ctr_nii = Image(fname_res), Image(centerline_filename)
# crop image around the spinal cord centerline
sct.log.info("\nCropping the image around the spinal cord...")
fname_crop = sct.add_suffix(fname_res, '_crop')
crop_size = 48
X_CROP_LST, Y_CROP_LST, im_crop_nii = crop_image_around_centerline(im_in=im_nii,
ctr_in=ctr_nii,
crop_size=crop_size)
del ctr_nii
# normalize the intensity of the images
sct.log.info("Normalizing the intensity...")
im_norm_in = apply_intensity_normalization(img=im_crop_nii, contrast=contrast_type)
del im_crop_nii
# resample to 0.5mm isotropic
fname_norm = sct.add_suffix(fname_orient, '_norm')
im_norm_in.save(fname_norm)
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_norm, fname_res3d, '0.5x0.5x0.5',
'mm', 'linear', verbose=0)
# segment data using 3D convolutions
sct.log.info("\nSegmenting the MS lesions using deep learning on 3D patches...")
segmentation_model_fname = os.path.join(path_sct, 'data', 'deepseg_lesion_models', '{}_lesion.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_lesionseg')
segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
fname_in=fname_res3d,
fname_out=fname_seg_crop_res)
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_seg_crop_res, fname_seg_res2d, initial_2d_resolution,
'mm', 'linear', verbose=0)
seg_crop_data = Image(fname_seg_res2d).data
# reconstruct the segmentation from the crop data
sct.log.info("\nReassembling the image...")
seg_uncrop_nii = uncrop_image(ref_in=im_nii,
data_crop=seg_crop_data,
x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST)
fname_seg_res_RPI = sct.add_suffix(file_fname, '_res_RPI_seg')
seg_uncrop_nii.save(fname_seg_res_RPI)
del seg_uncrop_nii, im_nii, seg_crop_data
# resample to initial resolution
sct.log.info("Resampling the segmentation to the original image resolution...")
initial_resolution = 'x'.join([str(input_resolution[0]), str(input_resolution[1]), str(input_resolution[2])])
fname_seg_RPI = sct.add_suffix(file_fname, '_RPI_seg')
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_seg_res_RPI, fname_seg_RPI, initial_resolution,
'mm', 'linear', verbose=0)
seg_initres_nii = Image(fname_seg_RPI)
# binarize the resampled image to remove interpolation effects
sct.log.info("\nBinarizing the segmentation to avoid interpolation effects...")
thr = 0.1
seg_initres_nii.data[np.where(seg_initres_nii.data >= thr)] = 1
seg_initres_nii.data[np.where(seg_initres_nii.data < thr)] = 0
# reorient to initial orientation
sct.log.info("\nReorienting the segmentation to the original image orientation...")
fname_seg = sct.add_suffix(file_fname, '_seg')
if original_orientation != 'RPI':
out_nii = msct_image.change_orientation(seg_initres_nii, original_orientation)
seg_initres_nii.save(fname_seg)
del seg_initres_nii
tmp_folder.chdir_undo()
# copy image from temporary folder into output folder
sct.copy(os.path.join(tmp_folder_path, fname_seg), output_folder)
# remove temporary files
if remove_temp_files:
sct.log.info("\nRemove temporary files...")
tmp_folder.cleanup()
return os.path.join(output_folder, fname_seg)
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):
# 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 dmri_moco(param):
file_data = 'dmri.nii'
file_data_dirname, file_data_basename, file_data_ext = sct.extract_fname(file_data)
file_b0 = 'b0.nii'
file_dwi = 'dwi.nii'
ext_data = '.nii.gz' # workaround "too many open files" by slurping the data
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups.nii'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), param.verbose)
# Identify b=0 and DWI images
index_b0, index_dwi, nb_b0, nb_dwi = sct_dmri_separate_b0_and_dwi.identify_b0('bvecs.txt', param.fname_bvals, param.bval_min, param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': Size of data (' + str(nt) + ') and size of bvecs (' + str(nb_b0 + nb_dwi) + ') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0_out = concat_data(im_b0_list, 3).save(file_b0)
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = sct.add_suffix(file_b0, '_mean')
sct.run(['sct_maths', '-i', file_b0, '-o', file_b0_mean, '-mean', 't'], param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup * param.group_size):((iGroup + 1) * param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi) - nb_remaining:len(index_dwi)])
file_dwi_dirname, file_dwi_basename, file_dwi_ext = sct.extract_fname(file_dwi)
# DWI groups
file_dwi_mean = []
for iGroup in tqdm(range(nb_groups), unit='iter', unit_scale=False, desc="Merge within groups", ascii=True, ncols=80):
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
file_dwi_merge_i = os.path.join(file_dwi_dirname, file_dwi_basename + '_' + str(iGroup) + ext_data)
im_dwi_list = []
for it in range(nb_dwi_i):
im_dwi_list.append(im_data_split_list[index_dwi_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3).save(file_dwi_merge_i)
# Average DW Images
file_dwi_mean_i = os.path.join(file_dwi_dirname, file_dwi_basename + '_mean_' + str(iGroup) + ext_data)
file_dwi_mean.append(file_dwi_mean_i)
sct.run(["sct_maths", "-i", file_dwi_merge_i, "-o", file_dwi_mean[iGroup], "-mean", "t"], 0)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(file_dwi_mean[iGroup])
im_dw_out = concat_data(im_dw_list, 3).save(file_dwi_group)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
sct.run(["sct_maths", "-i", file_dwi_group, "-o", file_dwi_group + '_mean' + ext_data, "-mean", "t"], param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...', param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group + '_seg.nii'
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'b0.nii'
# identify target image
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = os.path.join(file_data_dirname, file_data_basename + '_T' + str(index_b0[index_dwi[0] - 1]).zfill(4) + ext_data)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = os.path.join(file_data_dirname, file_data_basename + '_T' + str(index_b0[0]).zfill(4) + ext_data)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
file_mat_b0 = moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = file_dwi_mean[0] # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
file_mat_dwi = moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
# TODO: use file_mat_b0 and file_mat_dwi instead of the hardcoding below
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.copy('mat_b0groups/' + 'mat.Z0000T' + str(it).zfill(4) + ext_mat,
mat_final + 'mat.Z0000T' + str(index_b0[it]).zfill(4) + ext_mat)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(len(group_indexes[iGroup])): # we cannot use enumerate because group_indexes has 2 dim.
sct.copy('mat_dwigroups/' + 'mat.Z0000T' + str(iGroup).zfill(4) + ext_mat,
mat_final + 'mat.Z0000T' + str(group_indexes[iGroup][dwi]).zfill(4) + ext_mat)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0), param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_data
param_moco.file_target = os.path.join(file_dwi_dirname, file_dwi_basename + '_mean_' + str(0) + ext_data) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_dmri = Image(file_data)
fname_data_moco = os.path.join(file_data_dirname, file_data_basename + param.suffix + '.nii')
im_dmri_moco = Image(fname_data_moco)
im_dmri_moco.header = im_dmri.header
im_dmri_moco.save()
return os.path.abspath(fname_data_moco)