def main(args, temp_path, pool):
input_image = orig_input_image = args.input_image
# assigning default value of mask
mask = mask_93
#setting up output path
if args.output_path:
output_path = args.output_path
else:
output_path = os.path.dirname(orig_input_image)
#setting up the ROIs
if roi['param_all'] in args.roi_names:
labels = list(roi['label_names'])
else:
roi_dict = dict(zip(roi['param_names'], roi['label_names']))
labels = [roi_dict[el] for el in args.roi_names]
#setting up the template
if args.algorithm == "v2":
if args.template is not None and args.mask is not None:
template = args.template
mask = args.mask
print "Custom template and mask"
elif args.template is not None and args.mask is None:
sys.exit("!!!!!!! Both template and mask need to be specified simultaneously and they need to be of the same size !!!!!!!")
elif args.template is None and args.mask is not None:
sys.exit("!!!!!!! Both template and mask need to be specified simultaneously and they need to be of the same size !!!!!!!")
else:
template = template_93
mask = mask_93
print "Algorithm is v2"
elif args.algorithm == "v1":
sys.exit("!!!!!!! v1 algorithm not yet implemented !!!!!!!")
elif args.algorithm == "v0":
template = orig_template
print "Template is origtemplate.nii.gz"
else:
sys.exit("!!!!!!! Algorithm incorrectly specified !!!!!!!")
# print 'Template being used is'
# print os.path.abspath(template)
# TODO prevent both jointfusion and majority voting being set
# if args.jointfusion is None:
# print "args.jointfusion has been set (value is %s)" % args.jointfusion
# if args.majorityvoting is None:
# print "args.majorityvoting has been set (value is %s)" % args.majorityvoting
# sys.exit("!!!!!!! Only one label fusion can be selected at any time (default is antsJointFusion) !!!!!!!")
if args.warp:
warp_path = args.warp
else:
# TODO remove this as the default behavior, instead do ANTS?
head, tail = os.path.split(input_image)
tail = tail.replace('.nii', '').replace('.gz', '') #split('.', 1)[0]
warp_path = os.path.join(temp_path, tail)
t = time.time()
if args.algorithm == "v2":
# Crop the input
# Affine registering template to input
ants_rigid_registration(orig_input_image, orig_template)
print "Completed a quick rigid registration of input and full template"
mask_input = os.path.join(os.path.dirname(orig_input_image), 'mask_inp.nii.gz')
# Transform mask from template space to input space
ants_ApplyTransforms(mask, orig_input_image, mask_input)
#ants_WarpImageMultiTransform(mask, mask_input, orig_input_image)
print "Completed transforming the mask from template space to input space"
file_name = os.path.basename(orig_input_image)
index_of_dot = file_name.index('.')
file_name_without_extension = file_name[:index_of_dot]
input_image = os.path.join(os.path.dirname(orig_input_image), 'crop_'+file_name_without_extension+'.nii.gz')
# Cropping input using this mask
parallel_command(crop_by_mask(orig_input_image, input_image, mask_input))
print 'Completed cropping the input. Elapsed: %s' % timedelta(seconds=time.time()-t)
# FSL automatically converts .nii to .nii.gz
sanitized_image = os.path.join(temp_path, os.path.basename(input_image) + ('.gz' if input_image.endswith('.nii') else ''))
print '--- Reorienting image. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
if not os.path.exists(sanitized_image):
input_image = sanitize_input(input_image, sanitized_image, parallel_command)
if args.right:
print '--- Flipping along L-R. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
flip_lr(input_image, input_image, parallel_command)
print '--- Correcting bias. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
bias_correct(input_image, input_image, **exec_options)
else:
print 'Skipped, using %s' % sanitized_image
input_image = sanitized_image
print '--- Registering to mean brain template. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
if args.forcereg or not check_warps(warp_path):
if args.warp:
print 'Saving output as %s' % warp_path
else:
warp_path = os.path.join(temp_path, tail)
print 'Saving output to temporary path.'
# ants_nonlinear_registration(template, input_image, warp_path, **exec_options)
print 'temppath %s warppath %s input_image %s' % (temp_path, warp_path, input_image)
if args.algorithm == "v2":
ants_new_nonlinear_registration(template, input_image, warp_path, **exec_options)
else:
ants_v0_nonlinear_registration(template, input_image, warp_path, **exec_options)
else:
print 'Skipped, using %sInverseWarp.nii.gz and %sAffine.txt' % (warp_path, warp_path)
# generating the warped output
registered = os.path.join(temp_path, 'registered.nii.gz')
cmd = 'WarpImageMultiTransform 3 %s %s -R %s %s1Warp.nii.gz %s0GenericAffine.mat' % (input_image, registered, template, warp_path, warp_path)
parallel_command(cmd)
print '--- Warping prior labels and images. --- Elapsed: %s' % timedelta(seconds=time.time() - t)
# TODO should probably use output from warp_atlas_subject instead of hard coding paths in create_atlas
# TODO make this more parallel
warped_labels = pool.map(partial(
warp_atlas_subject,
path=prior_path,
# TODO cleanup this hack to always have whole thalamus so can estimate mask
labels=set(labels + ['1-THALAMUS']),
input_image=input_image,
input_transform_prefix=warp_path,
output_path=temp_path,
exec_options=exec_options,
), subjects)
warped_labels = {label: {subj: d[label] for subj, d in zip(subjects, warped_labels)} for label in warped_labels[0]}
# # print '--- Forming subject-registered atlases. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
# atlases = pool.map(partial(create_atlas, path=temp_path, subjects=subjects, target='', echo=exec_options['echo']),
# [{'label': label, 'output_atlas': os.path.join(temp_path, label+'_atlas.nii.gz')} for label in warped_labels])
# atlases = dict(zip(warped_labels, zip(*atlases)[0]))
# atlas_image = atlases['WMnMPRAGE_bias_corr']
atlas_images = warped_labels['WMnMPRAGE_bias_corr'].values()
print '--- Performing label fusion. --- Elapsed: %s' % timedelta(seconds=time.time() - t)
# FIXME use whole-brain template registration optimized parameters instead, these are from crop pipeline
optimal_picsl = optimal['PICSL']
# for k, v in warped_labels.iteritems():
# print k, v
# for label in labels:
# print optimal_picsl[label]
if args.jointfusion:
pool.map(partial(label_fusion_picsl, input_image, atlas_images),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label + '.nii.gz'),
rp=optimal_picsl[label]['rp'],
rs=optimal_picsl[label]['rs'],
beta=optimal_picsl[label]['beta'],
**exec_options
) for label in labels])
elif args.majorityvoting:
pool.map(partial(label_fusion_majority),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label + '.nii.gz'),
**exec_options
) for label in labels])
else:
# Estimate mask to restrict computation
mask = os.path.join(temp_path, 'mask.nii.gz')
check_run(
mask,
conservative_mask,
warped_labels['1-THALAMUS'].values(),
mask,
dilation=10,
)
pool.map(partial(label_fusion_picsl_ants, input_image, atlas_images),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label + '.nii.gz'),
rp=optimal_picsl[label]['rp'],
rs=optimal_picsl[label]['rs'],
beta=optimal_picsl[label]['beta'],
mask=mask,
**exec_options
) for label in labels])
# STEPS
# pool_small.map(partial(label_fusion, input_image=input_image, image_atlas=atlases['WMnMPRAGE_bias_corr'], echo=exec_options['echo']),
# [{
# 'label_atlas': atlases[label],
# 'output_label': os.path.join(output_path, label+'.nii.gz'),
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# } for label in labels]
# )
# for label in labels:
# print {
# 'label': label,
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# }
# partial_fusion = partial(label_fusion, input_image=input_image, image_atlas=atlases['WMnMPRAGE_bias_corr'], echo=exec_options['echo'])
# label_fusion_args = {
# 'label_atlas': atlases[label],
# 'output_label': os.path.join(output_path, label+'.nii.gz'),
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# }
# partial_fusion(**label_fusion_args)
files = [(os.path.join(temp_path, label + '.nii.gz'), os.path.join(output_path, label + '.nii.gz')) for label in labels]
if args.right:
pool.map(flip_lr, files)
files = [(os.path.join(output_path, label + '.nii.gz'), os.path.join(output_path, label + '.nii.gz')) for label in labels]
# Resort output to original ordering
pool.map(parallel_command,
['%s %s %s %s' % (os.path.join(this_path, 'swapdimlike.py'), in_file, orig_input_image, out_file) for in_file, out_file in files])
# get the vlp file path for splitting
vlp_file = os.path.join(output_path, '6-VLP.nii.gz')
# Re-orient to standard space - LR PA IS format
san_vlp_file = os.path.join(output_path, 'san_6-VLP.nii.gz')
input_image1 = sanitize_input(vlp_file, san_vlp_file, parallel_command)
# get the sanitized vlp for processing
input_nii = nibabel.load(input_image1)
data = input_nii.get_data()
hdr = input_nii.get_header()
affine = input_nii.get_affine()
# Coronal axis for RL PA IS orientation
vlps = split_roi(data, None, 2)
for fname, sub_vlp in zip(['6_VLPv.nii.gz', '6_VLPd.nii.gz'], vlps):
output_nii = nibabel.Nifti1Image(sub_vlp, affine, hdr)
output_nii.to_filename(os.path.join(os.path.dirname(out_file), fname))
print '--- Finished --- Elapsed: %s' % timedelta(seconds=time.time() - t)
def main(args, temp_path, pool):
input_image = orig_input_image = args.input_image
output_path = args.output_path
if roi['param_all'] in args.roi_names:
labels = list(roi['label_names'])
else:
roi_dict = dict(zip(roi['param_names'], roi['label_names']))
labels = [roi_dict[el] for el in args.roi_names]
if args.warp:
warp_path = args.warp
else:
# TODO remove this as the default behavior, instead do ANTS?
head, tail = os.path.split(input_image)
tail = tail.replace('.nii', '').replace('.gz', '') #split('.', 1)[0]
warp_path = os.path.join(temp_path, tail)
t = time.time()
# FSL automatically converts .nii to .nii.gz
sanitized_image = os.path.join(temp_path, os.path.basename(input_image) + ('.gz' if input_image.endswith('.nii') else ''))
print '--- Reorienting image. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
if not os.path.exists(sanitized_image):
input_image = sanitize_input(input_image, sanitized_image, parallel_command)
if args.right:
print '--- Flipping along L-R. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
flip_lr(input_image, input_image, parallel_command)
print '--- Correcting bias. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
bias_correct(input_image, input_image, **exec_options)
else:
print 'Skipped, using %s' % sanitized_image
input_image = sanitized_image
print '--- Registering to mean brain template. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
if args.forcereg or not check_warps(warp_path):
if args.warp:
print 'Saving output as %s' % warp_path
else:
warp_path = os.path.join(temp_path, tail)
print 'Saving output to temporary path.'
ants_nonlinear_registration(template, input_image, warp_path, **exec_options)
else:
print 'Skipped, using %sInverseWarp.nii.gz and %sAffine.txt' % (warp_path, warp_path)
print '--- Warping prior labels and images. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
# TODO should probably use output from warp_atlas_subject instead of hard coding paths in create_atlas
# TODO make this more parallel
warped_labels = pool.map(partial(
warp_atlas_subject,
path=prior_path,
# TODO cleanup this hack to always have whole thalamus so can estimate mask
labels=set(labels + ['1-THALAMUS']),
input_image=input_image,
input_transform_prefix=warp_path,
output_path=temp_path,
exec_options=exec_options,
), subjects)
warped_labels = {label: {subj: d[label] for subj, d in zip(subjects, warped_labels)} for label in warped_labels[0]}
# print '--- Forming subject-registered atlases. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
# atlases = pool.map(partial(create_atlas, path=temp_path, subjects=subjects, target='', echo=exec_options['echo']),
# [{'label': label, 'output_atlas': os.path.join(temp_path, label+'_atlas.nii.gz')} for label in warped_labels])
# atlases = dict(zip(warped_labels, zip(*atlases)[0]))
# atlas_image = atlases['WMnMPRAGE_bias_corr']
atlas_images = warped_labels['WMnMPRAGE_bias_corr'].values()
print '--- Performing label fusion. --- Elapsed: %s' % timedelta(seconds=time.time() - t)
# FIXME use whole-brain template registration optimized parameters instead, these are from crop pipeline
optimal_picsl = optimal['PICSL']
# for k, v in warped_labels.iteritems():
# print k, v
# for label in labels:
# print optimal_picsl[label]
if args.jointfusion:
pool.map(partial(label_fusion_picsl, input_image, atlas_images),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label+'.nii.gz'),
rp=optimal_picsl[label]['rp'],
rs=optimal_picsl[label]['rs'],
beta=optimal_picsl[label]['beta'],
**exec_options
) for label in labels])
else:
# Estimate mask to restrict computation
mask = os.path.join(temp_path, 'mask.nii.gz')
check_run(
mask,
conservative_mask,
warped_labels['1-THALAMUS'].values(),
mask,
dilation=10,
)
pool.map(partial(label_fusion_picsl_ants, input_image, atlas_images),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label + '.nii.gz'),
rp=optimal_picsl[label]['rp'],
rs=optimal_picsl[label]['rs'],
beta=optimal_picsl[label]['beta'],
mask=mask,
**exec_options
) for label in labels])
# STEPS
# pool_small.map(partial(label_fusion, input_image=input_image, image_atlas=atlases['WMnMPRAGE_bias_corr'], echo=exec_options['echo']),
# [{
# 'label_atlas': atlases[label],
# 'output_label': os.path.join(output_path, label+'.nii.gz'),
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# } for label in labels]
# )
# for label in labels:
# print {
# 'label': label,
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# }
# partial_fusion = partial(label_fusion, input_image=input_image, image_atlas=atlases['WMnMPRAGE_bias_corr'], echo=exec_options['echo'])
# label_fusion_args = {
# 'label_atlas': atlases[label],
# 'output_label': os.path.join(output_path, label+'.nii.gz'),
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# }
# partial_fusion(**label_fusion_args)
files = [(os.path.join(temp_path, label + '.nii.gz'), os.path.join(output_path, label + '.nii.gz')) for label in labels]
if args.right:
pool.map(flip_lr, files)
files = [(os.path.join(output_path, label + '.nii.gz'), os.path.join(output_path, label + '.nii.gz')) for label in labels]
# Resort output to original ordering
pool.map(parallel_command,
['%s %s %s %s' % (os.path.join(this_path, 'swapdimlike.py'), in_file, orig_input_image, out_file) for in_file, out_file in files])
print '--- Finished --- Elapsed: %s' % timedelta(seconds=time.time() - t)
def main(args, temp_path, pool):
input_image = orig_input_image = args.input_image
output_path = args.output_path
if roi['param_all'] in args.roi_names:
labels = list(roi['label_names'])
else:
roi_dict = dict(zip(roi['param_names'], roi['label_names']))
labels = [roi_dict[el] for el in args.roi_names]
if args.warp:
warp_path = args.warp
else:
# TODO remove this as the default behavior, instead do ANTS?
head, tail = os.path.split(input_image)
tail = tail.replace('.nii', '').replace('.gz', '') #split('.', 1)[0]
warp_path = os.path.join(temp_path, tail)
t = time.time()
# FSL automatically converts .nii to .nii.gz
sanitized_image = os.path.join(temp_path, os.path.basename(input_image) + ('.gz' if input_image.endswith('.nii') else ''))
print '--- Reorienting image. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
if not os.path.exists(sanitized_image):
input_image = sanitize_input(input_image, sanitized_image, parallel_command)
if args.right:
print '--- Flipping along L-R. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
flip_lr(input_image, input_image, parallel_command)
print '--- Correcting bias. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
bias_correct(input_image, input_image, **exec_options)
else:
print 'Skipped, using %s' % sanitized_image
input_image = sanitized_image
print '--- Registering to mean brain template. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
if args.forcereg or not check_warps(warp_path):
if args.warp:
print 'Saving output as %s' % warp_path
else:
warp_path = os.path.join(temp_path, tail)
print 'Saving output to temporary path.'
ants_nonlinear_registration(template, input_image, warp_path, **exec_options)
else:
print 'Skipped, using %sInverseWarp.nii.gz and %sAffine.txt' % (warp_path, warp_path)
print '--- Warping prior labels and images. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
# TODO should probably use output from warp_atlas_subject instead of hard coding paths in create_atlas
# TODO make this more parallel
warped_labels = pool.map(partial(
warp_atlas_subject,
path=prior_path,
# TODO cleanup this hack to always have whole thalamus so can estimate mask
labels=set(labels + ['1-THALAMUS']),
input_image=input_image,
input_transform_prefix=warp_path,
output_path=temp_path,
exec_options=exec_options,
), subjects)
warped_labels = {label: {subj: d[label] for subj, d in zip(subjects, warped_labels)} for label in warped_labels[0]}
# print '--- Forming subject-registered atlases. --- Elapsed: %s' % timedelta(seconds=time.time()-t)
# atlases = pool.map(partial(create_atlas, path=temp_path, subjects=subjects, target='', echo=exec_options['echo']),
# [{'label': label, 'output_atlas': os.path.join(temp_path, label+'_atlas.nii.gz')} for label in warped_labels])
# atlases = dict(zip(warped_labels, zip(*atlases)[0]))
# atlas_image = atlases['WMnMPRAGE_bias_corr']
atlas_images = warped_labels['WMnMPRAGE_bias_corr'].values()
print '--- Performing label fusion. --- Elapsed: %s' % timedelta(seconds=time.time() - t)
# FIXME use whole-brain template registration optimized parameters instead, these are from crop pipeline
optimal_picsl = optimal['PICSL']
# for k, v in warped_labels.iteritems():
# print k, v
# for label in labels:
# print optimal_picsl[label]
if args.jointfusion:
pool.map(partial(label_fusion_picsl, input_image, atlas_images),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label+'.nii.gz'),
rp=optimal_picsl[label]['rp'],
rs=optimal_picsl[label]['rs'],
beta=optimal_picsl[label]['beta'],
**exec_options
) for label in labels])
else:
# Estimate mask to restrict computation
mask = os.path.join(temp_path, 'mask.nii.gz')
check_run(
mask,
conservative_mask,
warped_labels['1-THALAMUS'].values(),
mask,
dilation=10,
)
pool.map(partial(label_fusion_picsl_ants, input_image, atlas_images),
[dict(
atlas_labels=warped_labels[label].values(),
output_label=os.path.join(temp_path, label + '.nii.gz'),
rp=optimal_picsl[label]['rp'],
rs=optimal_picsl[label]['rs'],
beta=optimal_picsl[label]['beta'],
mask=mask,
**exec_options
) for label in labels])
# STEPS
# pool_small.map(partial(label_fusion, input_image=input_image, image_atlas=atlases['WMnMPRAGE_bias_corr'], echo=exec_options['echo']),
# [{
# 'label_atlas': atlases[label],
# 'output_label': os.path.join(output_path, label+'.nii.gz'),
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# } for label in labels]
# )
# for label in labels:
# print {
# 'label': label,
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# }
# partial_fusion = partial(label_fusion, input_image=input_image, image_atlas=atlases['WMnMPRAGE_bias_corr'], echo=exec_options['echo'])
# label_fusion_args = {
# 'label_atlas': atlases[label],
# 'output_label': os.path.join(output_path, label+'.nii.gz'),
# 'sigma': optimal_steps[label]['steps_sigma'],
# 'X': optimal_steps[label]['steps_X'],
# 'mrf': optimal_steps[label]['steps_mrf'],
# }
# partial_fusion(**label_fusion_args)
files = [(os.path.join(temp_path, label + '.nii.gz'), os.path.join(output_path, label + '.nii.gz')) for label in labels]
if args.right:
pool.map(flip_lr, files)
files = [(os.path.join(output_path, label + '.nii.gz'), os.path.join(output_path, label + '.nii.gz')) for label in labels]
# Resort output to original ordering
pool.map(parallel_command,
['%s %s %s %s' % (os.path.join(this_path, 'swapdimlike.py'), in_file, orig_input_image, out_file) for in_file, out_file in files])
print '--- Finished --- Elapsed: %s' % timedelta(seconds=time.time() - t)