def warp_atlas_subject(subject,
path,
labels,
input_image,
input_transform_prefix,
output_path,
exec_options={}):
"""
Warp a training set subject's labels to input_image.
"""
a_transform_prefix = os.path.join(path, subject + '/WMnMPRAGE')
output_path = os.path.join(output_path, subject)
try:
os.mkdir(output_path)
except OSError:
# Exists
pass
combined_warp = os.path.join(output_path, 'Warp.nii.gz')
if not os.path.exists(combined_warp):
check_run(combined_warp,
ants_compose_a_to_b,
a_transform_prefix,
b_path=input_image,
b_transform_prefix=input_transform_prefix,
output=combined_warp,
**exec_options)
output_labels = {}
# OPT parallelize, or merge parallelism with subject level
for label in labels:
label_fname = os.path.join(path, subject, 'sanitized_rois',
label + '.nii.gz')
warped_label = os.path.join(output_path, label + '.nii.gz')
switches = '--use-NN'
check_run(warped_label,
ants_apply_only_warp,
template=input_image,
input_image=label_fname,
input_warp=combined_warp,
output_image=warped_label,
switches=switches,
**exec_options)
output_labels[label] = warped_label
# Warp anatomical WMnMPRAGE_bias_corr too
# TODO merge this into previous for loop to be DRY?
output_labels['WMnMPRAGE_bias_corr'] = output_image = os.path.join(
output_path, image_name)
if not os.path.exists(output_labels['WMnMPRAGE_bias_corr']):
print output_labels['WMnMPRAGE_bias_corr']
check_run(output_image,
ants_apply_only_warp,
template=input_image,
input_image=os.path.join(path, subject, image_name),
input_warp=combined_warp,
output_image=output_image,
switches='--use-BSpline',
**exec_options)
return output_labels
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 input to template
print "1. Linear Registration of input and template \n"
t1 = time.time()
ants_linear_registration(orig_template, orig_input_image)
print '1. ----- Time Elapsed: %s \n \n' % timedelta(
seconds=time.time() - t1)
mask_input = os.path.join(os.path.dirname(orig_input_image),
'mask_inp.nii.gz')
print "2. Transform mask from template space to input space \n"
t1 = time.time()
# Transform mask from template space to input space
ants_WarpImageMultiTransform(mask, mask_input, orig_input_image)
print '2. ----- Time Elapsed: %s \n \n' % timedelta(
seconds=time.time() - t1)
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
print "3. Cropping input using this mask \n"
t1 = time.time()
parallel_command(
crop_by_mask(orig_input_image, input_image, mask_input))
print '3. ----- Time Elapsed: %s \n \n' % timedelta(
seconds=time.time() - t1)
# 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 ''))
if not os.path.exists(sanitized_image):
input_image = sanitize_input(input_image, sanitized_image,
parallel_command)
if args.right:
print "4. lipping along L-R \n"
t1 = time.time()
flip_lr(input_image, input_image, parallel_command)
print '4. ----- Time Elapsed: %s \n \n' % timedelta(
seconds=time.time() - t1)
print "5. Correcting bias \n"
t1 = time.time()
bias_correct(input_image, input_image, **exec_options)
print '5. ----- Time Elapsed: %s \n \n' % timedelta(
seconds=time.time() - t1)
else:
print 'Skipped, using %s' % sanitized_image
input_image = sanitized_image
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)
# generating the warped output
print "6. Warping prior labels and images \n \n"
t1 = time.time()
registered = os.path.join(temp_path, 'registered.nii.gz')
cmd = 'WarpImageMultiTransform 3 %s %s -R %s %sWarp.nii.gz %sAffine.txt' % (
input_image, registered, template, warp_path, warp_path)
parallel_command(cmd)
print '6. --- Elapsed: %s \n \n' % timedelta(seconds=time.time() - t1)
t1 = time.time()
# 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 \n \n' % timedelta(
seconds=time.time() - t1)
# 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'),
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
])
print '!! --- Performing label fusion. --- Elapsed: %s \n \n' % timedelta(
seconds=time.time() - t1)
# 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.template_size == 61:
template = template_61
mask = mask_61
if args.right:
sys.exit("!!!!!!! Feature not implemented (61 with -R option) !!!!!!!")
else:
template = template_93
mask = mask_93
# 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()
# Crop the input
# Affine registering input to template
ants_linear_registration(orig_template, orig_input_image)
mask_input = os.path.join(os.path.dirname(orig_input_image), 'mask_inp.nii.gz')
# Transform mask from template space to input space
ants_WarpImageMultiTransform(mask, mask_input, orig_input_image)
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))
# 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)
registered = os.path.join(temp_path, 'registered.nii.gz')
cmd = 'WarpImageMultiTransform 3 %s %s -R %s %sWarp.nii.gz %sAffine.txt' % (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'),
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)
