def test_straighten():
"""Test straightening with default params"""
fname_t2 = sct_test_path('t2', 't2.nii.gz') # sct_download_data -d sct_testing_data
fname_t2_seg = sct_test_path('t2', 't2_seg-manual.nii.gz')
sc_straight = SpinalCordStraightener(fname_t2, fname_t2_seg)
sc_straight.accuracy_results = True
sc_straight.straighten()
assert sc_straight.mse_straightening < 0.8
assert sc_straight.max_distance_straightening < 1.2
def test_straighten():
"""Test straightening with default params"""
fname_t2 = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2.nii.gz') # sct_download_data -d sct_testing_data
fname_t2_seg = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2_seg.nii.gz')
sc_straight = SpinalCordStraightener(fname_t2, fname_t2_seg)
sc_straight.accuracy_results = True
sc_straight.straighten()
assert sc_straight.mse_straightening < 0.8
assert sc_straight.max_distance_straightening < 1.2
def test_straighten():
"""Test straightening with default params"""
fname_t2 = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2.nii.gz'
) # sct_download_data -d sct_testing_data
fname_t2_seg = os.path.join(sct.__sct_dir__,
'sct_testing_data/t2/t2_seg.nii.gz')
sc_straight = SpinalCordStraightener(fname_t2, fname_t2_seg)
sc_straight.accuracy_results = True
sc_straight.straighten()
assert sc_straight.mse_straightening < 0.8
assert sc_straight.max_distance_straightening < 1.2
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(argv=None):
"""
Main function
:param argv:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
input_filename = arguments.i
centerline_file = arguments.s
sc_straight = SpinalCordStraightener(input_filename, centerline_file)
if arguments.dest is not None:
sc_straight.use_straight_reference = True
sc_straight.centerline_reference_filename = str(arguments.dest)
if arguments.ldisc_input is not None:
if not sc_straight.use_straight_reference:
printv(
'Warning: discs position are not taken into account if reference is not provided.'
)
else:
sc_straight.discs_input_filename = str(arguments.ldisc_input)
sc_straight.precision = 4.0
if arguments.ldisc_dest is not None:
if not sc_straight.use_straight_reference:
printv(
'Warning: discs position are not taken into account if reference is not provided.'
)
else:
sc_straight.discs_ref_filename = str(arguments.ldisc_dest)
sc_straight.precision = 4.0
# Handling optional arguments
sc_straight.remove_temp_files = arguments.r
sc_straight.interpolation_warp = arguments.x
sc_straight.output_filename = arguments.o
sc_straight.path_output = arguments.ofolder
path_qc = arguments.qc
sc_straight.verbose = verbose
# if arguments.cpu_nb is not None:
# sc_straight.cpu_number = arguments.cpu-nb)
if arguments.disable_straight2curved:
sc_straight.straight2curved = False
if arguments.disable_curved2straight:
sc_straight.curved2straight = False
if arguments.speed_factor:
sc_straight.speed_factor = arguments.speed_factor
if arguments.xy_size:
sc_straight.xy_size = arguments.xy_size
sc_straight.param_centerline = ParamCenterline(
algo_fitting=arguments.centerline_algo,
smooth=arguments.centerline_smooth)
if arguments.param is not None:
params_user = arguments.param
# update registration parameters
for param in params_user:
param_split = param.split('=')
if param_split[0] == 'precision':
sc_straight.precision = float(param_split[1])
if param_split[0] == 'threshold_distance':
sc_straight.threshold_distance = float(param_split[1])
if param_split[0] == 'accuracy_results':
sc_straight.accuracy_results = int(param_split[1])
if param_split[0] == 'template_orientation':
sc_straight.template_orientation = int(param_split[1])
fname_straight = sc_straight.straighten()
printv("\nFinished! Elapsed time: {} s".format(sc_straight.elapsed_time),
verbose)
# Generate QC report
if path_qc is not None:
path_qc = os.path.abspath(path_qc)
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
generate_qc(fname_straight,
args=arguments,
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
process=os.path.basename(__file__.strip('.py')))
display_viewer_syntax([fname_straight], 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']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param.straighten_fitting = arguments['-straighten-fitting']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct.run(['sct_apply_transfo', '-i', ftmp_seg, '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.algo_fitting = param.straighten_fitting
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
s, o = sct.run(['sct_concat_transfo', '-w', 'warp_straight2curve.nii.gz', '-d', ftmp_data, '-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run(['sct_apply_transfo', '-i', ftmp_label, '-o', add_suffix(ftmp_label, '_straight'), '-d', add_suffix(ftmp_seg, '_straight'), '-w', 'warp_curve2straight.nii.gz', '-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run(['sct_concat_transfo', '-w', 'warp_curve2straight.nii.gz,straight2templateAffine.txt', '-d', 'template.nii', '-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run(['sct_apply_transfo', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_straightAffine'), '-d', ftmp_template, '-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run(['sct_apply_transfo', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_straightAffine'), '-d', ftmp_template, '-w', 'warp_curve2straightAffine.nii.gz', '-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct.run(['sct_apply_transfo', '-i', src_seg, '-d', dest_seg, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run(['sct_concat_transfo', '-w', 'warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward), '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz'], verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse) + ',warp_straight2curve.nii.gz', '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
else:
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse) + ',-straight2templateAffine.txt,warp_straight2curve.nii.gz', '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_forward), '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse), '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz'], verbose)
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = get_parser()
arguments = parser.parse(args)
# assigning variables to arguments
input_filename = arguments["-i"]
centerline_file = arguments["-s"]
sc_straight = SpinalCordStraightener(input_filename, centerline_file)
if "-dest" in arguments:
sc_straight.use_straight_reference = True
sc_straight.centerline_reference_filename = str(arguments["-dest"])
if "-ldisc_input" in arguments:
if not sc_straight.use_straight_reference:
sct.printv('Warning: discs position are not taken into account if reference is not provided.')
else:
sc_straight.discs_input_filename = str(arguments["-ldisc_input"])
sc_straight.precision = 4.0
if "-ldisc_dest" in arguments:
if not sc_straight.use_straight_reference:
sct.printv('Warning: discs position are not taken into account if reference is not provided.')
else:
sc_straight.discs_ref_filename = str(arguments["-ldisc_dest"])
sc_straight.precision = 4.0
# Handling optional arguments
if "-r" in arguments:
sc_straight.remove_temp_files = int(arguments["-r"])
if "-x" in arguments:
sc_straight.interpolation_warp = str(arguments["-x"])
if "-o" in arguments:
sc_straight.output_filename = str(arguments["-o"])
if '-ofolder' in arguments:
sc_straight.path_output = arguments['-ofolder']
else:
sc_straight.path_output = './'
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
sc_straight.verbose = verbose
# if "-cpu-nb" in arguments:
# sc_straight.cpu_number = int(arguments["-cpu-nb"])
if '-disable-straight2curved' in arguments:
sc_straight.straight2curved = False
if '-disable-curved2straight' in arguments:
sc_straight.curved2straight = False
if '-speed_factor' in arguments:
sc_straight.speed_factor = arguments['-speed_factor']
if '-xy_size' in arguments:
sc_straight.xy_size = arguments['-xy_size']
if "-param" in arguments:
params_user = arguments['-param']
# update registration parameters
for param in params_user:
param_split = param.split('=')
if param_split[0] == 'algo_fitting':
sc_straight.algo_fitting = param_split[1]
if param_split[0] == 'degree':
sc_straight.degree = int(param_split[1])
if param_split[0] == 'precision':
sc_straight.precision = float(param_split[1])
if param_split[0] == 'threshold_distance':
sc_straight.threshold_distance = float(param_split[1])
if param_split[0] == 'accuracy_results':
sc_straight.accuracy_results = int(param_split[1])
if param_split[0] == 'template_orientation':
sc_straight.template_orientation = int(param_split[1])
fname_straight = sc_straight.straighten()
sct.printv("\nFinished! Elapsed time: {} s".format(sc_straight.elapsed_time), verbose)
sct.display_viewer_syntax([fname_straight], verbose=verbose)