def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param_centerline = ParamCenterline(
algo_fitting=arguments['-centerline-algo'],
smooth=arguments['-centerline-smooth'])
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg, param_centerline)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-w', 'warp_curve2straight.nii.gz',
'-d', 'straight_ref.nii.gz',
'-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.param_centerline = param_centerline
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct_concat_transfo.main(args=[
'-w', 'warp_straight2curve.nii.gz',
'-d', ftmp_data,
'-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_label,
'-o', add_suffix(ftmp_label, '_straight'),
'-d', add_suffix(ftmp_seg, '_straight'),
'-w', 'warp_curve2straight.nii.gz',
'-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct_concat_transfo.main(args=[
'-w', ['warp_curve2straight.nii.gz', 'straight2templateAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_data,
'-o', add_suffix(ftmp_data, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-o', add_suffix(ftmp_seg, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz',
'-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct_apply_transfo.main(args=[
'-i', src_seg,
'-d', dest_seg,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations: anat --> template
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
warp_forward.insert(0, 'warp_curve2straightAffine.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Concatenate transformations: template --> anat
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
else:
warp_inverse.append('straight2templateAffine.txt')
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['straight2templateAffine.txt'],
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['template2subjectAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
# file_template_label = param.file_template_label
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# retrieve template file names
file_template_vertebral_labeling = get_file_label(
os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(
os.path.join(path_template, 'template'),
contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'),
'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(
path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template',
file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template',
'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(remove_temp_files), verbose)
# check if data, segmentation and landmarks are in the same space
# JULIEN 2017-04-25: removed because of issue #1168
# sct.printv('\nCheck if data, segmentation and landmarks are in the same space...')
# if not sct.check_if_same_space(fname_data, fname_seg):
# sct.printv('ERROR: Data image and segmentation are not in the same space. Please check space and orientation of your files', verbose, 'error')
# if not sct.check_if_same_space(fname_data, fname_landmarks):
# sct.printv('ERROR: Data image and landmarks are not in the same space. Please check space and orientation of your files', verbose, 'error')
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# ftmp_template_label_disc = 'template_label_disc.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.run([
'sct_convert', '-i', fname_data, '-o',
os.path.join(path_tmp, ftmp_data)
])
sct.run([
'sct_convert', '-i', fname_seg, '-o',
os.path.join(path_tmp, ftmp_seg)
])
sct.run([
'sct_convert', '-i', fname_landmarks, '-o',
os.path.join(path_tmp, ftmp_label)
])
sct.run([
'sct_convert', '-i', fname_template, '-o',
os.path.join(path_tmp, ftmp_template)
])
sct.run([
'sct_convert', '-i', fname_template_seg, '-o',
os.path.join(path_tmp, ftmp_template_seg)
])
sct_convert.main(args=[
'-i', fname_template_vertebral_labeling, '-o',
os.path.join(path_tmp, ftmp_template_label)
])
if label_type == 'disc':
sct_convert.main(args=[
'-i', fname_template_disc_labeling, '-o',
os.path.join(path_tmp, ftmp_template_label)
])
# sct.run('sct_convert -i '+fname_template_label+' -o '+os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling',
verbose)
sct_label_utils.main(args=[
'-i', ftmp_template_label, '-vert-body', '0', '-o',
ftmp_template_label
])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template',
verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(
sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv(
'ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) +
'\nLabel max from template: ' + str(labels_template[-1].value),
verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv(
'WARNING: Only one label is present. Forcing initial transformation to: '
+ paramreg.steps['0'].dof, 1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
sct.run(
['sct_maths', '-i', 'seg.nii.gz', '-bin', '0.5', '-o', 'seg.nii.gz'])
# smooth segmentation (jcohenadad, issue #613)
# sct.printv('\nSmooth segmentation...', verbose)
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
# jcohenadad: updated 2016-06-16: DO NOT smooth the seg anymore. Issue #
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 0 -o '+add_suffix(ftmp_seg, '_smooth'))
# ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run([
'sct_resample', '-i', ftmp_data, '-mm', '1.0x1.0x1.0', '-x',
'linear', '-o',
add_suffix(ftmp_data, '_1mm')
])
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run([
'sct_resample', '-i', ftmp_seg, '-mm', '1.0x1.0x1.0', '-x',
'linear', '-o',
add_suffix(ftmp_seg, '_1mm')
])
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run([
'sct_image', '-i', ftmp_data, '-setorient', 'RPI', '-o',
add_suffix(ftmp_data, '_rpi')
])
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run([
'sct_image', '-i', ftmp_seg, '-setorient', 'RPI', '-o',
add_suffix(ftmp_seg, '_rpi')
])
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run([
'sct_image', '-i', ftmp_label, '-setorient', 'RPI', '-o',
add_suffix(ftmp_label, '_rpi')
])
ftmp_label = add_suffix(ftmp_label, '_rpi')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(
sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [
coordinates_labels[0].z - offset_crop,
coordinates_labels[-1].z + offset_crop
]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
status_crop, output_crop = sct.run([
'sct_crop_image', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_crop'), '-dim', '2', '-start',
str(cropping_slices[0]), '-end',
str(cropping_slices[1])
], verbose)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
status_crop, output_crop = sct.run([
'sct_crop_image', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_crop'), '-dim', '2', '-bzmax'
], verbose)
cropping_slices = output_crop.split('Dimension 2: ')[1].split(
'\n')[0].split(' ')
# output: segmentation_rpi_crop.nii.gz
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# straighten segmentation
sct.printv(
'\nStraighten the spinal cord using centerline/segmentation...',
verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir,
"warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir,
"warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files = [ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(input_files=cache_input_files, )
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(
cachefile, cache_sig
) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(
fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv(
'Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct.run([
'sct_apply_transfo', '-i', ftmp_seg, '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz',
'-o',
add_suffix(ftmp_seg, '_straight')
])
else:
from sct_straighten_spinalcord import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run([
'sct_concat_transfo', '-w', 'warp_straight2curve.nii.gz', '-d',
ftmp_data, '-o', 'warp_straight2curve.nii.gz'
])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz',
'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv(
'\nRemove unused label on template. Keep only label present in the input label image...',
verbose)
sct.run([
'sct_label_utils', '-i', ftmp_template_label, '-o',
ftmp_template_label, '-remove', ftmp_label
])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run([
'sct_maths', '-i', ftmp_label, '-o',
add_suffix(ftmp_label, '_dilate'), '-dilate', '3'
])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run([
'sct_apply_transfo', '-i', ftmp_label, '-o',
add_suffix(ftmp_label, '_straight'), '-d',
add_suffix(ftmp_seg, '_straight'), '-w',
'warp_curve2straight.nii.gz', '-x', 'nn'
])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
try:
register_landmarks(ftmp_label,
ftmp_template_label,
paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt',
verbose=verbose)
except Exception:
sct.printv(
'ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/',
verbose=verbose,
type='error')
# Concatenate transformations: curve --> straight --> affine
sct.printv(
'\nConcatenate transformations: curve --> straight --> affine...',
verbose)
sct.run([
'sct_concat_transfo', '-w',
'warp_curve2straight.nii.gz,straight2templateAffine.txt', '-d',
'template.nii', '-o', 'warp_curve2straightAffine.nii.gz'
])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run([
'sct_apply_transfo', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_straightAffine'), '-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz'
])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run([
'sct_apply_transfo', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_straightAffine'), '-d', ftmp_template, '-w',
'warp_curve2straightAffine.nii.gz', '-x', 'linear'
])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(round(np.min(points_straight))), int(round(np.max(points_straight)))
sct.run('sct_crop_image -i ' + ftmp_seg + ' -start ' + str(min_point) + ' -end ' + str(max_point) + ' -dim 2 -b 0 -o ' + add_suffix(ftmp_seg, '_black'))
ftmp_seg = add_suffix(ftmp_seg, '_black')
"""
# binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run([
'sct_maths', '-i', ftmp_seg, '-bin', '0.5', '-o',
add_suffix(ftmp_seg, '_bin')
])
ftmp_seg = add_suffix(ftmp_seg, '_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...',
verbose)
sct.run([
'sct_crop_image', '-i', ftmp_template, '-o',
add_suffix(ftmp_template, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run([
'sct_crop_image', '-i', ftmp_template_seg, '-o',
add_suffix(ftmp_template_seg, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run([
'sct_crop_image', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run([
'sct_crop_image', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_crop'), '-dim', '2', '-start',
str(zmin_template), '-end',
str(zmax_template)
])
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...',
verbose)
sct.run([
'sct_resample', '-i', ftmp_template, '-o',
add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run([
'sct_resample', '-i', ftmp_template_seg, '-o',
add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run([
'sct_resample', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run([
'sct_resample', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv(
'\nEstimate transformation for step #' + str(i_step) + '...',
verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
# apply transformation from previous step, to use as new src for registration
sct.run([
'sct_apply_transfo', '-i', src, '-d', dest, '-w',
','.join(warp_forward), '-o',
add_suffix(src,
'_regStep' + str(i_step - 1)), '-x', interp_step
], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(
src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...',
verbose)
sct.run([
'sct_concat_transfo', '-w',
'warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward), '-d',
'template.nii', '-o', 'warp_anat2template.nii.gz'
], verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...',
verbose)
warp_inverse.reverse()
if vertebral_alignment:
sct.run([
'sct_concat_transfo', '-w',
','.join(warp_inverse) + ',warp_straight2curve.nii.gz', '-d',
'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
else:
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse) +
',-straight2templateAffine.txt,warp_straight2curve.nii.gz',
'-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run([
'sct_image', '-i', ftmp_data, '-setorient', 'RPI', '-o',
add_suffix(ftmp_data, '_rpi')
])
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run([
'sct_image', '-i', ftmp_seg, '-setorient', 'RPI', '-o',
add_suffix(ftmp_seg, '_rpi')
])
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run([
'sct_image', '-i', ftmp_label, '-setorient', 'RPI', '-o',
add_suffix(ftmp_label, '_rpi')
])
ftmp_label = add_suffix(ftmp_label, '_rpi')
# Remove unused label on template. Keep only label present in the input label image
sct.printv(
'\nRemove unused label on template. Keep only label present in the input label image...',
verbose)
sct.run([
'sct_label_utils', '-i', ftmp_template_label, '-o',
ftmp_template_label, '-remove', ftmp_label
])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue(
) # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x),
int(new_label.y),
int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.setFileName('label_rpi_modif.nii.gz')
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label,
ftmp_label,
paramreg.steps['0'].dof,
fname_affine=warp_forward[0],
verbose=verbose,
path_qc="./")
except Exception:
sct.printv(
'ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/',
verbose=verbose,
type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv(
'\nEstimate transformation for step #' + str(i_step) + '...',
verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run([
'sct_apply_transfo', '-i', src, '-d', dest, '-w',
','.join(warp_forward), '-o',
add_suffix(src,
'_regStep' + str(i_step - 1)), '-x', interp_step
], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(
src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...',
verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_forward), '-d',
'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
sct.printv('\nConcatenate transformations: subject --> template...',
verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse), '-d',
'template.nii', '-o', 'warp_anat2template.nii.gz'
], verbose)
# Apply warping fields to anat and template
sct.run([
'sct_apply_transfo', '-i', 'template.nii', '-o',
'template2anat.nii.gz', '-d', 'data.nii', '-w',
'warp_template2anat.nii.gz', '-crop', '1'
], verbose)
sct.run([
'sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz',
'-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(
os.path.join(path_tmp, "warp_template2anat.nii.gz"),
os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_anat2template.nii.gz"),
os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"),
fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"),
fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(
os.path.join(path_tmp, "warp_curve2straight.nii.gz"),
os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_straight2curve.nii.gz"),
os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "straight_ref.nii.gz"),
os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's',
verbose)
if param.path_qc is not None:
generate_qc(fname_data, fname_template2anat, fname_seg, args,
os.path.abspath(param.path_qc))
sct.display_viewer_syntax([fname_data, fname_template2anat],
verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template],
verbose=verbose)
def vertebral_detection(fname,
fname_seg,
contrast,
param,
init_disc,
verbose=1,
path_template='',
path_output='../',
scale_dist=1.):
"""
Find intervertebral discs in straightened image using template matching
:param fname: file name of straigthened spinal cord
:param fname_seg: file name of straigthened spinal cord segmentation
:param contrast: t1 or t2
:param param: advanced parameters
:param init_disc:
:param verbose:
:param path_template:
:param path_output: output path for verbose=2 pictures
:param scale_dist: float: Scaling factor to adjust average distance between two adjacent intervertebral discs
:return:
"""
sct.printv('\nLook for template...', verbose)
sct.printv('Path template: ' + path_template, verbose)
# adjust file names if MNI-Poly-AMU template is used (by default: PAM50)
fname_level = get_file_label(os.path.join(path_template, 'template'),
'vertebral labeling',
output='filewithpath')
fname_template = get_file_label(os.path.join(path_template, 'template'),
contrast.upper() + '-weighted template',
output='filewithpath')
# Open template and vertebral levels
sct.printv('\nOpen template and vertebral levels...', verbose)
data_template = Image(fname_template).data
data_disc_template = Image(fname_level).data
# open anatomical volume
im_input = Image(fname)
data = im_input.data
# smooth data
data = gaussian_filter(data,
param.smooth_factor,
output=None,
mode="reflect")
# get dimension of src
nx, ny, nz = data.shape
# define xc and yc (centered in the field of view)
xc = int(np.round(nx / 2)) # direction RL
yc = int(np.round(ny / 2)) # direction AP
# get dimension of template
nxt, nyt, nzt = data_template.shape
# define xc and yc (centered in the field of view)
xct = int(np.round(nxt / 2)) # direction RL
yct = int(np.round(nyt / 2)) # direction AP
# define mean distance (in voxel) between adjacent discs: [C1/C2 -> C2/C3], [C2/C3 -> C4/C5], ..., [L1/L2 -> L2/L3]
centerline_level = data_disc_template[xct, yct, :]
# attribute value to each disc. Starts from max level, then decrease.
# NB: value 2 means disc C2/C3 (and so on and so forth).
min_level = centerline_level[centerline_level.nonzero()].min()
max_level = centerline_level[centerline_level.nonzero()].max()
list_disc_value_template = list(range(min_level, max_level))
# add disc above top one
list_disc_value_template.insert(int(0), min_level - 1)
sct.printv('\nDisc values from template: ' + str(list_disc_value_template),
verbose)
# get diff to find transitions (i.e., discs)
diff_centerline_level = np.diff(centerline_level)
# get disc z-values
list_disc_z_template = diff_centerline_level.nonzero()[0].tolist()
list_disc_z_template.reverse()
sct.printv('Z-values for each disc: ' + str(list_disc_z_template), verbose)
list_distance_template = (
np.diff(list_disc_z_template) *
(-1)).tolist() # multiplies by -1 to get positive distances
# Update distance with scaling factor
list_distance_template = [i * scale_dist for i in list_distance_template]
sct.printv(
'Distances between discs (in voxel): ' + str(list_distance_template),
verbose)
# display init disc
if verbose == 2:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# get percentile for automatic contrast adjustment
data_display = np.mean(data[xc - param.size_RL:xc +
param.size_RL, :, :],
axis=0).transpose()
percmin = np.percentile(data_display, 10)
percmax = np.percentile(data_display, 90)
# display image
plt.matshow(data_display,
fignum=50,
cmap=plt.cm.gray,
clim=[percmin, percmax],
origin='lower')
plt.title('Anatomical image')
plt.autoscale(
enable=False) # to prevent autoscale of axis when displaying plot
plt.figure(50), plt.scatter(yc + param.shift_AP_visu,
init_disc[0],
c='yellow',
s=50)
plt.text(yc + param.shift_AP_visu + 4,
init_disc[0],
str(init_disc[1]) + '/' + str(init_disc[1] + 1),
verticalalignment='center',
horizontalalignment='left',
color='pink',
fontsize=15), plt.draw()
# plt.ion() # enables interactive mode
# FIND DISCS
# ===========================================================================
sct.printv('\nDetect intervertebral discs...', verbose)
# assign initial z and disc
current_z = init_disc[0]
current_disc = init_disc[1]
# create list for z and disc
list_disc_z = []
list_disc_value = []
zrange = list(range(-10, 10))
direction = 'superior'
search_next_disc = True
while search_next_disc:
sct.printv(
'Current disc: ' + str(current_disc) + ' (z=' + str(current_z) +
'). Direction: ' + direction, verbose)
try:
# get z corresponding to current disc on template
current_z_template = list_disc_z_template[current_disc]
except:
# in case reached the bottom (see issue #849)
sct.printv(
'WARNING: Reached the bottom of the template. Stop searching.',
verbose, 'warning')
break
# find next disc
# N.B. Do not search for C1/C2 disc (because poorly visible), use template distance instead
if current_disc != 1:
current_z = compute_corr_3d(data,
data_template,
x=xc,
xshift=0,
xsize=param.size_RL,
y=yc,
yshift=param.shift_AP,
ysize=param.size_AP,
z=current_z,
zshift=0,
zsize=param.size_IS,
xtarget=xct,
ytarget=yct,
ztarget=current_z_template,
zrange=zrange,
verbose=verbose,
save_suffix='_disc' +
str(current_disc),
gaussian_std=999,
path_output=path_output)
# display new disc
if verbose == 2:
plt.figure(50), plt.scatter(yc + param.shift_AP_visu,
current_z,
c='yellow',
s=50)
plt.text(yc + param.shift_AP_visu + 4,
current_z,
str(current_disc) + '/' + str(current_disc + 1),
verticalalignment='center',
horizontalalignment='left',
color='yellow',
fontsize=15), plt.draw()
# append to main list
if direction == 'superior':
# append at the beginning
list_disc_z.insert(0, current_z)
list_disc_value.insert(0, current_disc)
elif direction == 'inferior':
# append at the end
list_disc_z.append(current_z)
list_disc_value.append(current_disc)
# adjust correcting factor based on already-identified discs
if len(list_disc_z) > 1:
# compute distance between already-identified discs
list_distance_current = (np.diff(list_disc_z) * (-1)).tolist()
# retrieve the template distance corresponding to the already-identified discs
index_disc_identified = [
i for i, j in enumerate(list_disc_value_template)
if j in list_disc_value[:-1]
]
list_distance_template_identified = [
list_distance_template[i] for i in index_disc_identified
]
# divide subject and template distances for the identified discs
list_subject_to_template_distance = [
float(list_distance_current[i]) /
list_distance_template_identified[i]
for i in range(len(list_distance_current))
]
# average across identified discs to obtain an average correcting factor
correcting_factor = np.mean(list_subject_to_template_distance)
sct.printv('.. correcting factor: ' + str(correcting_factor),
verbose)
else:
correcting_factor = 1
# update list_distance specific for the subject
list_distance = [
int(np.round(list_distance_template[i] * correcting_factor))
for i in range(len(list_distance_template))
]
# assign new current_z and disc value
if direction == 'superior':
try:
approx_distance_to_next_disc = list_distance[
list_disc_value_template.index(current_disc - 1)]
except ValueError:
sct.printv(
'WARNING: Disc value not included in template. Using previously-calculated distance: '
+ str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z + approx_distance_to_next_disc
current_disc = current_disc - 1
elif direction == 'inferior':
try:
approx_distance_to_next_disc = list_distance[
list_disc_value_template.index(current_disc)]
except:
sct.printv(
'WARNING: Disc value not included in template. Using previously-calculated distance: '
+ str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z - approx_distance_to_next_disc
current_disc = current_disc + 1
# if current_z is larger than searching zone, switch direction (and start from initial z minus approximate
# distance from updated template distance)
if current_z >= nz or current_disc == 0:
sct.printv('.. Switching to inferior direction.', verbose)
direction = 'inferior'
current_disc = init_disc[1] + 1
current_z = init_disc[0] - list_distance[
list_disc_value_template.index(current_disc)]
# if current_z is lower than searching zone, stop searching
if current_z <= 0:
search_next_disc = False
# if upper disc is not 1, add disc above top disc based on mean_distance_adjusted
upper_disc = min(list_disc_value)
# if not upper_disc == 1:
sct.printv(
'Adding top disc based on adjusted template distance: #' +
str(upper_disc - 1), verbose)
approx_distance_to_next_disc = list_distance[
list_disc_value_template.index(upper_disc - 1)]
next_z = max(list_disc_z) + approx_distance_to_next_disc
sct.printv('.. approximate distance: ' + str(approx_distance_to_next_disc),
verbose)
# make sure next disc does not go beyond FOV in superior direction
if next_z > nz:
list_disc_z.insert(0, nz)
else:
list_disc_z.insert(0, next_z)
# assign disc value
list_disc_value.insert(0, upper_disc - 1)
# Label segmentation
label_segmentation(fname_seg,
list_disc_z,
list_disc_value,
verbose=verbose)
# save figure
if verbose == 2:
plt.figure(50), plt.savefig(
os.path.join(path_output, "fig_anat_straight_with_labels.png"))
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)