#for sigma in [8,3,1,20]:
for sigma in [8]:
original_atlas_img = atlas.get_original(kernel=sigma)
###### Final atlas -> subject registration
registration_parameters = dict(
metric='CC',
radiusBins=4,
regularization='Gauss[%0.3f,%0.3f]' %
(regularization, regularization2),
method='nonlinear',
nonlinear_iterations='30x90x21',
)
forward_reg = pb.ANTSCommand(
"Register label-blurred flair atlas with sigma %d to subject" %
sigma,
moving=original_atlas_img,
fixed=subj_final_img,
output_folder=os.path.join(dataset.get_folder(subj=subj), 'reg'),
mask=mask,
**registration_parameters)
registrations['low_resolution'] = forward_reg
#for mod in [modifiers, '_prep_pad']:
mod = modifiers
for ratio in xrange(1, 7):
upsampler = pb.NiiToolsUpsampleCommand(
"Upsample image with mask and linear interp",
input=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers),
output=dataset.get(subj=subj,
moving = atlas.get_original(feature='flairTemplateInBuckner_sigma8')
seg = atlas.get_original(feature='buckner61_seg')
fixed = data.get_original(feature='template')
mask = data.get_original(feature='mask')
print(moving)
print(seg)
print(fixed)
print(mask)
for (i, r1) in enumerate(r1s):
for (j, r2) in enumerate(r2s):
masked_reg = pb.ANTSCommand('Registration with mask',
moving=moving,
fixed=fixed,
regularization='Gauss[%0.3f,%0.3f]' %
(r1, r2),
metric='CC',
radiusBins=32,
output_folder=data.get_folder(feature=''),
method='200x200x200',
mask=mask)
warp = pb.ANTSWarpCommand.make_from_registration(
'Warp segmentation using registration',
moving=seg,
reference=fixed,
registration=masked_reg,
output_folder=data.get_folder(feature=''),
inversion='forward',
useNN=True,
)
warped_labels = warp.outfiles[0]
def main(argv):
########################
### Argument parsing ###
########################
USAGE = '%s <subj> <smoothness regularization> <field regularization> <out folder> [<subj list>]' % argv[
0]
if len(argv) not in [5, 6]:
print(USAGE)
raise ValueError
subj = argv[1]
# Regularization parameters for ANTS
regularization = float(argv[2])
regularization2 = float(argv[3])
# where the data lives
data_subfolder = argv[4]
if 'site00' in data_subfolder:
site = 'site00'
else:
site = 'unknown'
if len(argv) == 6:
subject_list = open(argv[5]).read().split()
mode = 'server'
else:
mode = 'execute'
#############################
### Set up atlas and data ###
#############################
BASE = os.path.join(PROCESSING_ROOT, data_subfolder)
#BASE = os.path.join(DATA_ROOT, 'processed_datasets', data_subfolder)
#SCRATCH_BASE = os.path.join(SCRATCH_ROOT, 'processed_datasets', data_subfolder)
#SCRATCH_BASE = BASE
#BASE = os.path.join('/data/vision/scratch/polina/users/rameshvs', data_subfolder)
## Atlas
#atlas = pb.Dataset(ATLAS_BASE, 'buckner61{feature}{extension}', None)
#atlas = pb.Dataset(ATLAS_BASE, 'flair_template{extension}', None)
#atlas = pb.Dataset(ATLAS_BASE, 'flairTemplateInBuckner_sigma{kernel}{extension}', None)
atlas = pb.Dataset(ATLAS_BASE,
'flairTemplateInBuckner_sigma{kernel}{extension}', None)
buckner = pb.Dataset(ATLAS_BASE, 'buckner61{feature}{extension}', None)
## Subject data
dataset = pb.Dataset(
BASE,
# How are the inputs to the pipeline stored?
os.path.join(
BASE, '{subj}/original/{modality}_1/{subj}_{modality}_{feature}'),
# How should intermediate files be stored?
#os.path.join(BASE, '{subj}/images/{subj}_{modality}_{feature}{modifiers}'),
os.path.join(BASE,
'{subj}/images/{subj}_{modality}_{feature}{modifiers}'),
log_template=os.path.join(BASE, '{subj}/logs/'),
)
#dataset.add_mandatory_input(modality='t1', feature='raw')
#dataset.add_mandatory_input(modality='flair', feature='img')
dataset.add_mandatory_input(modality='flair', feature='raw')
dataset.get_original(subj=subj, modality='t1', feature='raw')
if mode == 'server':
tracking.run_server(subject_list, dataset)
raise ValueError
else:
pass
#############################
### Registration pipeline ###
#############################
###
flair_input = dataset.get_original(subj=subj,
modality='flair',
feature='raw')
dwi_input = dataset.get_original(subj=subj, modality='dwi', feature='raw')
if True: #site in ['site00', 'site13', 'site18']:
modifiers = '_prep_pad'
first_step = pb.PyPadCommand(
"Pad flair",
#cmdName=os.path.join(cwd, 'strip_header.py'),
input=flair_input,
output=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers),
out_mask=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers + '_mask_seg'),
clobber=True,
)
else:
raise NotImplementedError
mask = dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers + '_brainmask')
robex = pb.RobexCommand("Brain extraction with ROBEX",
input=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers),
output=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers + '_robex'),
out_mask=mask)
masker = pb.NiiToolsMaskCommand(
"Apply mask from robex",
input=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers),
mask=mask,
output=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers + '_brain'),
)
modifiers += '_brain'
# intensity_corr = pb.MCCMatchWMCommand(
# "Intensity correction for flair image",
# inFile=dataset.get(subj=subj, modality='flair', feature='img', modifiers=modifiers),
# maskFile=mask,
# intensity=FLAIR_INTENSITY,
# output=dataset.get(subj=subj, modality='flair', feature='img', modifiers=modifiers + '_matchwm'))
intensity_corr = pb.NiiToolsMatchIntensityCommand(
"Intensity correction for flair image",
inFile=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers),
maskFile=mask,
intensity=FLAIR_INTENSITY,
output=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers + '_matchwm'),
)
modifiers += '_matchwm'
subj_final_img = dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers)
dwi_mask = dataset.get(subj=subj,
modality='flair',
feature='mask',
modifiers='_from_flair')
for sigma in [8]:
atlas_img = atlas.get_original(kernel=sigma)
basic_threshold_segmentation_wmh = dataset.get(
subj=subj,
modality='flair',
feature='wmh_raw_threshold_seg',
modifiers='')
basic_threshold_segmentation_stroke = dataset.get(
subj=subj,
modality='flair',
feature='wmh_raw_threshold_seg',
modifiers='')
multimodal_registration = pb.ANTSCommand(
"Rigidly register DWI to FLAIR",
moving=dwi_input,
fixed=subj_final_img,
output_folder=os.path.join(dataset.get_folder(subj=subj), 'reg'),
metric='MI',
radiusBins=32,
mask=mask,
method='rigid',
)
pb.ANTSWarpCommand.make_from_registration(
"Warp mask to DWI",
moving=mask,
reference=dwi_input,
output_filename=dwi_mask,
registration=multimodal_registration,
inversion='forward')
###### Final atlas -> subject registration
forward_reg = pb.ANTSCommand(
"Register label-blurred flair atlas with sigma %d to subject" %
sigma,
moving=atlas_img,
fixed=subj_final_img,
output_folder=os.path.join(dataset.get_folder(subj=subj), 'reg'),
metric='CC',
radiusBins=4,
mask=mask,
regularization='Gauss[%0.3f,%0.3f]' %
(regularization, regularization2),
method='201x201x201',
)
pb.ANTSWarpCommand.make_from_registration(
"Warp subject image to atlas space using sigma %d warp" % sigma,
moving=subj_final_img,
reference=atlas_img,
output_filename=dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers='_in_atlas'),
registration=forward_reg,
inversion='inverse',
)
label_warp = pb.ANTSWarpCommand.make_from_registration(
"Warp atlas labels to subject space using sigma %d warp" % sigma,
moving=buckner.get_original(feature='_seg'),
reference=subj_final_img,
registration=forward_reg,
useNN=True,
)
dwi_seg = dataset.get(subj=subj,
modality='dwi',
feature='seg',
modifiers='')
dwi_is_dwi = dataset.get(subj=subj,
modality='dwi',
feature='verified',
modifiers='',
extension='.txt')
label_warp_dwi = pb.ANTSWarpCommand.make_from_registration_sequence(
"Warp atlas labels to dwi",
moving=buckner.get_original(feature='_seg'),
reference=dwi_input,
output_filename=dwi_seg,
reg_sequence=[forward_reg, multimodal_registration],
inversion_sequence=['forward', 'inverse'],
useNN=True,
)
pb.PyFunctionCommand("Verify ventricles greater than white matter",
function="flairpipe.check_fluid_attenuation",
args=[dwi_input, dwi_seg, dwi_is_dwi],
output_positions=[2])
dwi_matchwm = dataset.get(subj=subj,
modality='dwi',
feature='img',
modifiers='_matchwm')
intensity_corr_dwi = pb.NiiToolsMatchIntensityCommand(
"Intensity correction for DWI image",
inFile=dwi_input,
maskFile=dwi_mask,
intensity=DWI_INTENSITY,
output=dwi_matchwm,
)
pb.ANTSWarpCommand.make_from_registration(
"Warp atlas image to subject space using sigma %d warp" % sigma,
moving=atlas_img,
reference=subj_final_img,
output_filename=dataset.get(subj=subj,
modality='atlas',
feature='img',
modifiers='_in_subject'),
registration=forward_reg)
# threshold_segmentation_dwi = pb.NiiToolsMaskedThresholdCommand(
# "Threshold segmentation for stroke",
# infile=dwi_matchwm,
# threshold=STROKE_THRESHOLD,
# output=basic_threshold_segmentation_stroke,
# label=dwi_seg,
# direction='greater',
# labels=[2,41],
# )
# threshold_segmentation_dwi_count = pb.NiiToolsMaskedThresholdCountCommand(
# "Threshold segmentation for stroke computation",
# infile=dwi_matchwm,
# threshold=STROKE_THRESHOLD,
# output=dataset.get(subj=subj, modality='other', feature='stroke_raw_threshold_seg', modifiers='', extension='.txt'),
# label=dwi_seg,
# direction='greater',
# units='mm',
# labels=[2,41],
# )
threshold_segmentation = pb.NiiToolsMaskedThresholdCommand(
"Threshold segmentation",
infile=intensity_corr.outfiles[0],
threshold=WMH_THRESHOLD,
output=basic_threshold_segmentation_wmh,
label=label_warp.outfiles[0],
direction='greater',
labels=[2, 41],
)
threshold_segmentation_count = pb.NiiToolsMaskedThresholdCountCommand(
"Threshold segmentation computation",
infile=intensity_corr.outfiles[0],
threshold=WMH_THRESHOLD,
output=dataset.get(subj=subj,
modality='other',
feature='wmh_raw_threshold_seg',
modifiers='',
extension='.txt'),
label=label_warp.outfiles[0],
direction='greater',
units='mm',
labels=[2, 41],
)
threshold_seg_to_atlas = pb.ANTSWarpCommand.make_from_registration(
"Warp threshold segmentation to atlas space",
moving=basic_threshold_segmentation_wmh,
reference=atlas_img,
registration=forward_reg,
output_filename=dataset.get(subj=subj,
modality='wmh_threshold_seg',
feature='in_atlas',
modifiers=''),
inversion='inverse')
filename = os.path.basename(label_warp.outfiles[0]).split('.')[0]
#subj_png_filename = dataset.get(subj=subj, modality='other', feature=filename, modifiers='', extension='.png')
subj_png_filename = dataset.get(subj=subj,
modality='other',
feature='buckner_labels',
modifiers='',
extension='.png')
pb.PyFunctionCommand(
"Generate flair with buckner label overlay",
"tools.better_overlay",
[
subj_final_img, label_warp.outfiles[0],
[15, 17, 19, 20, 21, 22, 23, 25], subj_png_filename
],
output_positions=[3],
)
for path in [
os.path.join(BASE, subj, 'images'),
os.path.join(BASE, subj, 'images', 'reg'),
dataset.get_log_folder(subj=subj)
]:
try:
os.mkdir(path)
except:
pass
### Generate script file and SGE qsub file
tracker = tracking.Tracker(pb.Command.all_commands,
pb.Dataset.all_datasets)
tracker.compute_dependencies()
###
# NIPYPE_ROOT = '/data/scratch/rameshvs/sandbox/nipype_regpipe'
# wf = tracker.make_nipype_pipeline(NIPYPE_ROOT)
log_folder = dataset.get_log_folder(subj=subj)
# pb.Command.generate_code_from_datasets([dataset, atlas], log_folder, subj, sge=False,
# wait_time=0, tracker=tracker)
site = 'site' + data_subfolder.split('site')[-1][:2]
subjects_file = '/data/vision/polina/projects/stroke/work/subject_lists/sites/%s.txt' % site
subjects = open(subjects_file).read().split()[1:]
aggregate_json = '/data/vision/polina/projects/stroke/work/rameshvs/site_pipeline_data_v2/%s.json' % site
if not os.path.exists(aggregate_json):
tracker.gather_multi_subject_jsons(subjects, subj, aggregate_json)
import json
tracking.run_server(subjects, dataset, json.load(open(aggregate_json)))
subj_final_img = dataset.get(subj=subj,
modality='flair',
feature='img',
modifiers=modifiers)
#for sigma in [8,3,1,20]:
for sigma in [8]:
atlas_img = atlas.get_original(kernel=sigma)
###### Final atlas -> subject registration
forward_reg = pb.ANTSCommand(
"Register label-blurred flair atlas with sigma %d to subject" %
sigma,
moving=atlas_img,
fixed=subj_final_img,
output_folder=os.path.join(dataset.get_folder(subj=subj),
'reg'),
metric='CC',
radiusBins=4,
mask=mask,
regularization='Gauss[%0.3f,%0.3f]' %
(regularization, regularization2),
method='201x201x201',
)
#stroke_atlas_files['pca_sdm_{}'.format(pca)] = '_pcasdmv6_{}'.format(pca)
pb.ANTSWarpCommand.make_from_registration(
"Warp subject image to atlas space using sigma %d warp" %
sigma,
moving=subj_final_img,
reference=atlas_img,
registration=forward_reg,
inversion='inverse')
subj, 't1', 'raw'),
output=original_t1,
extra_args='t1')
t1_modifiers += '_prep'
###### N4 bias correction of T1.
pb.N4Command("N4 bias field correction for T1",
input=dataset.get_file(subj, 't1', 'img', t1_modifiers),
output=dataset.get_file(subj, 't1', 'img',
t1_modifiers + '_bcorr'))
t1_modifiers += '_bcorr'
###### Initial rigid registration
initial_affine_reg = pb.ANTSCommand(
"Initial affine registration step: atlas->subj T1",
moving=atlas.get_file('img'),
fixed=dataset.get_file(subj, 't1', 'img', t1_modifiers),
metric='MI',
method='rigid')
mask_warp = pb.ANTSWarpCommand.make_from_registration(
"Warp atlas mask into subj space using initial"
"affine subj T1->atlas registration", atlas.get_file('mask'),
dataset.get_file(subj, 't1', 'img', t1_modifiers),
[initial_affine_reg], ['forward'])
###### Intensity matching (can't do histogram equalization)
# TODO use more robust mean-shift based mode-matching
pb.PyMatchWMCommand("Match white matter intensity values",
inFile=dataset.get_file(subj, 't1', 'img',
t1_modifiers),
maskFile=mask_warp.outfiles[0],
modifiers += '_matchwm'
subj_final_img = dataset.get(subj=subj, modality='flair', feature='img', modifiers=modifiers)
dwi_mask = dataset.get(subj=subj, modality='flair', feature='mask', modifiers='_from_flair')
for sigma in [8]:
atlas_img = atlas.get_original(kernel=sigma)
basic_threshold_segmentation_wmh = dataset.get(subj=subj, modality='flair', feature='wmh_raw_threshold_seg', modifiers='')
basic_threshold_segmentation_stroke = dataset.get(subj=subj, modality='flair', feature='wmh_raw_threshold_seg', modifiers='')
multimodal_registration = pb.ANTSCommand("Rigidly register DWI to FLAIR",
moving=dwi_input,
fixed=subj_final_img,
output_folder=os.path.join(dataset.get_folder(subj=subj), 'reg'),
metric='MI',
radiusBins=32,
mask=mask,
method='rigid',
)
pb.ANTSWarpCommand.make_from_registration(
"Warp mask to DWI",
moving=mask,
reference=dwi_input,
output_filename=dwi_mask,
registration=multimodal_registration,
inversion='forward'
)
###### Final atlas -> subject registration
forward_reg = pb.ANTSCommand(