def swi_link_inputs(swi_info, link_to_dir, change_to_dir=True):
util.mkcd_dir([link_to_dir], change_to_dir)
input_files = swi_info['inputs']
util.link_inputs(input_files + label_files, link_to_dir)
def results(ants_ct_info, verbose=False):
# Create results directory and populate
util.mkcd_dir([ants_ct_info['base']['results']], True)
files_to_link = [
(ants_ct_info['segment']['p_wm'], ants_ct_info['results']['p_wm']),
(ants_ct_info['segment']['segment'],
ants_ct_info['results']['segment']),
(ants_ct_info['register']['ct'], ants_ct_info['results']['ct']),
(ants_ct_info['register']['subject_to_template_affine'],
ants_ct_info['results']['subject_to_template_affine']),
(ants_ct_info['register']['subject_to_template_warp'],
ants_ct_info['results']['subject_to_template_warp']),
(ants_ct_info['register']['subject_to_template_jacobian'],
ants_ct_info['results']['subject_to_template_jacobian']),
(ants_ct_info['register']['template_to_subject_affine'],
ants_ct_info['results']['template_to_subject_affine']),
(ants_ct_info['register']['template_to_subject_warp'],
ants_ct_info['results']['template_to_subject_warp'])
]
for ii in files_to_link:
util.force_hard_link(ii[0], ii[1])
fsl_command = [
'fslmaths', ants_ct_info['segment']['p_gm_cortical'], '-add',
ants_ct_info['segment']['p_gm_subcortical'],
ants_ct_info['results']['p_gm']
]
util.iw_subprocess(fsl_command)
methods_write_json_redcap_ants_ct_instrument(ants_ct_info, verbose)
def methods_02_lpa(input_dir, verbose):
cenc_dirs = cenc.directories(input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
util.mkcd_dir([cenc_dirs['wmlesions']['dirs']['lpa']], True)
util.copy_inputs([
os.path.join(cenc_dirs['wmlesions']['dirs']['register'],
't2flair_Affine_nu__t2flair_Warped.nii.gz')
], cenc_dirs['wmlesions']['dirs']['lpa'])
glob_files = glob.glob('*.gz')
for ii in glob_files:
os.system('gunzip ' + ii)
os.chmod(str.replace(ii, '.gz', ''),
stat.S_IRUSR | stat.S_IRGRP | stat.S_IWUSR | stat.S_IWGRP)
# Run Matlab
command = ['cenc_wmlesions_run.sh', cenc_dirs['wmlesions']['dirs']['lpa']]
util.iw_subprocess(command, verbose, verbose, False)
os.chdir(cenc_dirs['wmlesions']['dirs']['lpa'])
for ii in glob.glob('*.nii'):
os.system('gzip ' + ii)
os.chmod(str.replace(ii, '.nii', '.nii.gz'),
stat.S_IRUSR | stat.S_IRGRP | stat.S_IWUSR | stat.S_IWGRP)
def methods_02_stats(config, verbose=False):
input_dir = config['dirs']['input']
util.mkcd_dir([config['methods']['results']['dir']], True)
command = [
'fslmaths',
os.path.join(config['dirs']['methods']['01-register'],
'nu_brain.nii.gz'), '-bin',
os.path.join(config['dirs']['methods']['results'], 'brain_mask.nii.gz')
]
util.iw_subprocess(command, verbose, verbose, False)
for ii in ['swi', 'swi_magnitude', 'swi_phase']:
command = [
'fslmaths',
os.path.join(config['dirs']['methods']['01-register'],
'swi_Affine_nu__' + ii + '.nii.gz'), '-mas',
'brain_mask.nii.gz', ii + '.nii.gz'
]
util.iw_subprocess(command, verbose, verbose, False)
util.force_hard_link(
os.path.join(config['methods']['input']['dir'], 'nu.nii.gz'),
'nu.nii.gz')
util.force_hard_link(
os.path.join(config['methods']['input']['dir'], 'nu_brain.nii.gz'),
'nu_brain.nii.gz')
def swi_link_inputs(input_dir, link_to_dir, change_to_dir=True):
cenc_dirs = cenc.directories(input_dir)
util.mkcd_dir([link_to_dir], change_to_dir)
input_files = cenc_dirs['swi']['inputs']
label_files = cenc_dirs['swi']['labels']
util.link_inputs(input_files + label_files, link_to_dir)
def methods_02_stats(input_dir, verbose=False):
# Register MT images to nu.nii.gz
cenc_dirs = cenc.directories(input_dir)
util.mkcd_dir([cenc_dirs['mt']['dirs']['02-stats']], True)
methods_write_json_redcap_mt_instrument(input_dir, verbose)
return
def results(input_dir):
""" Gather results and write the MagTran JSON output file"""
cenc_dirs = cenc.directories(input_dir)
swi_input_dir = cenc_dirs['swi']['dirs']['input']
swi_01_register_dir = cenc_dirs['swi']['dirs']['register']
swi_results_dir = cenc_dirs['swi']['dirs']['results']
util.mkcd_dir([swi_results_dir], True)
result_files = [[
os.path.join(cenc_dirs['swi']['dirs']['register'],
'swi_Affine_nu__swi_m0_Warped.nii.gz'),
os.path.join(cenc_dirs['swi']['dirs']['results'],
'swi_Affine_nu__swi_m0.nii.gz')
],
[
os.path.join(cenc_dirs['swi']['dirs']['register'],
'swi_Affine_nu__swi_m1_Warped.nii.gz'),
os.path.join(cenc_dirs['swi']['dirs']['results'],
'swi_Affine_nu__swi_m1.nii.gz')
],
[
os.path.join(cenc_dirs['swi']['dirs']['results'],
'magtrans.json'),
os.path.join(cenc_dirs['swi']['dirs']['results'],
'magtrans.json')
],
[
os.path.join(cenc_dirs['swi']['dirs']['register'],
'swir.nii.gz'),
os.path.join(cenc_dirs['swi']['dirs']['results'],
'swir.nii.gz')
],
[
os.path.join(cenc_dirs['swi']['dirs']['input'],
'nu.nii.gz'),
os.path.join(cenc_dirs['swi']['dirs']['results'],
'nu.nii.gz')
],
[
os.path.join(cenc_dirs['swi']['dirs']['register'],
'swir.nii.gz'),
os.path.join(cenc_dirs['results']['dirs']['images'],
'swir.nii.gz')
]]
for ii in result_files:
util.force_hard_link(ii[0], ii[1])
return
def prepare(input_dir):
cenc_dirs = cenc.directories(input_dir)
util.mkcd_dir([cenc_dirs['freesurfer']['input']])
input_files = [
os.path.join(cenc_dirs['cenc']['reorient'], 't1w.nii.gz'),
os.path.join(cenc_dirs['cenc']['reorient'], 't2flair.nii.gz'),
os.path.join(cenc_dirs['cenc']['reorient'], 't2tse.nii.gz')
]
util.link_inputs(input_files, cenc_dirs['freesurfer']['input'])
return
def main():
## Parsing Arguments
#
#
usage = "usage: %prog [options] arg1 arg2"
parser = argparse.ArgumentParser(prog='create_pial_mask')
parser.add_argument("subject_id", help="Subject ID", default=os.getcwd())
parser.add_argument("--subjects_dir",
help="Subject's Directory (default=$SUBJECTS_DIR)",
default=os.getenv('SUBJECTS_DIR'))
parser.add_argument('--out', help='Output filename', default='mask.nii.gz')
parser.add_argument('--outdir', help='Output directory', default='./')
parser.add_argument(
'-r',
'--reference',
help=
'Reference file to create mask with same orientation and image dimensions (None)',
default=None)
parser.add_argument('-v',
'--verbose',
help="Verbose flag",
action="store_true",
default=False)
parser.add_argument('--keep',
help="Keep intermediate files",
action="store_true",
default=False)
inArgs = parser.parse_args()
output_directory = os.path.abspath(inArgs.outdir)
reference_with_full_path = os.path.abspath(inArgs.reference)
util.mkcd_dir(output_directory, cd_flag=False)
create_pial_mask(inArgs.subject_id,
inArgs.subjects_dir,
inArgs.out,
output_directory,
reference=reference_with_full_path,
verbose=inArgs.verbose,
save=inArgs.keep)
def methods_00_input(config, verbose):
""" Renames inputs from configureation file to standard naming convention"""
if verbose:
print
print(config['config']['dirs'])
print
print(config['methods']['input'])
print
util.mkcd_dir([config['methods']['input']['dir']], True)
input_output_pairs = zip(config['methods']['input']['inputs'],
config['methods']['input']['outputs'])
for ii in input_output_pairs:
os.link(ii[0], ii[1])
def prepare( input_dir ):
cenc_dirs = cenc.directories( input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_input_dir = cenc_dirs['wmlesions']['dirs']['input']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
# Create Input Directory if it doesn't exist and link files
files = [ os.path.abspath( os.path.join( cenc_dirs['results']['dirs']['images'], 'nu.nii.gz')),
os.path.abspath( os.path.join( cenc_dirs['results']['dirs']['labels'], 'mask.nii.gz')),
os.path.abspath( os.path.join( cenc_dirs['cenc']['reorient'], 't2flair.nii.gz'))
]
util.mkcd_dir( os.path.join( wm_lesions_input_dir ) )
util.link_inputs( files, wm_lesions_input_dir )
def prepare(input_dir):
cenc_dirs = cenc.directories(input_dir)
util.mkcd_dir([cenc_dirs['freesurfer']['input']])
input_files = [os.path.join(cenc_dirs['cenc']['reorient'], 't1w.nii.gz')]
# Preliminary testing of Freesurfer with the T2FLAIR and T2_TSE has not been very successful.
# Removing them from the workflow.
#
# input_files = [ os.path.join( cenc_dirs['cenc']['reorient'], 't2flair.nii.gz'),
# os.path.join( cenc_dirs['cenc']['reorient'], 't2tse.nii.gz')
# ]
util.link_inputs(input_files, cenc_dirs['freesurfer']['input'])
return
def methods_01_register( input_dir, verbose ):
cenc_dirs = cenc.directories( input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
# Register
util.mkcd_dir( [ cenc_dirs['wmlesions']['dirs']['register'] ], True)
files = [ os.path.join( cenc_dirs['wmlesions']['dirs']['input'], 'nu.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['input'], 't2flair.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['labels'], 'mask.nii.gz')
]
util.link_inputs( files, cenc_dirs['wmlesions']['dirs']['register'] )
ants_register('t2flair')
def results( input_dir):
cenc_dirs = cenc.directories( input_dir)
util.mkcd_dir( [ cenc_dirs['wmlesions']['dirs']['results'] ], True)
# Link Files
link_result_files = [ [ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_t2flair.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_t2flair.nii.gz') ],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['input'], 'nu.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'nu.nii.gz') ],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_pmap.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions.json'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions.json')],
[os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_labels.nii.gz')],
[os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.csv'),
os.path.join(cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_labels.csv')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_mask.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_mask.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_pmap.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['images'], 'wmlesions_lpa_pmap.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_mask.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['labels'], 'wmlesions_lpa_mask.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_labels.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['labels'], 'wmlesions_lpa_labels.nii.gz')
]
]
for ii in link_result_files:
util.force_hard_link( ii[0], ii[1])
if inArgs.verbose:
print('\nCENC ID: ' + id)
print('CENC participant directory : ', participant_dir)
print('CENC nifti directory : ', nifti_dir)
print('CENC fMRI tarball : ', os.path.join(out_dir, tarball_filename))
print('\n')
# Create Output Directory if it doesn't exist
files = [
os.path.abspath(os.path.join(dicom_dir, 'dcmConvertAll.cfg')),
os.path.abspath(os.path.join(dicom_dir, 'dcmConvert_cenc.cfg')),
os.path.abspath(os.path.join(nifti_dir, 'rest.nii.gz'))
]
util.mkcd_dir(out_dir)
util.link_inputs(files, out_dir)
files_to_be_tarred = glob.glob('*')
# Create TarBall
tarball = tarfile.open(tarball_filename, 'w:gz')
for f in files_to_be_tarred:
tarball.add(f)
tarball.close()
# Clean Directory
def methods_03_stats(input_dir, verbose=False, min_lesion_volume=10):
cenc_dirs = cenc.directories(input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
util.mkcd_dir([cenc_dirs['wmlesions']['dirs']['stats']], True)
wm_lesions_stats_filename = os.path.join(
cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.csv')
# Create labels file from LPA probability map
iw_labels_label.create(
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'],
'ples_lpa_mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'))
# Measure statistics of labels
iw_labels_stats.measure(
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'),
None,
False, ['volume_mm3'],
wm_lesions_stats_filename,
limits_volume_voxels=[min_lesion_volume, numpy.inf],
limits_bb_volume_voxels=[0, numpy.inf],
limits_fill_factor=[0, 1],
sort='volume_mm3',
verbose=verbose,
verbose_nlines=20)
# Keep labels greater than 10 mm^3. Limit is set above
iw_labels_keep.keep(
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'), [],
wm_lesions_stats_filename,
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'))
# 1)
# 2) Create a WM Lesions mask
# 3)
command = [[
'fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'), '-bin', '-mul',
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'],
'ples_lpa_mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas',
os.path.join(cenc_dirs['wmlesions']['dirs']['input'], 'mask.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_pmap.nii.gz')
],
[
'fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'), '-bin',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_mask.nii.gz')
],
[
'fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'],
'mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas',
os.path.join(cenc_dirs['wmlesions']['dirs']['input'],
'mask.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_t2flair.nii.gz')
]]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Write out JSON file with RedCap Instrument
methods_write_json_redcap_instrument(input_dir, wm_lesions_stats_filename,
verbose)
def methods_03_stats(input_dir,
lower_threshold=0.5,
upper_threshold=numpy.inf,
min_lesion_volume_voxels=10,
verbose=False):
cenc_dirs = cenc.directories(input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
util.mkcd_dir([cenc_dirs['wmlesions']['dirs']['stats']], True)
wm_lesions_stats_filename = os.path.join(
cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.csv')
wm_lesions_pmap_stats_filename = os.path.join(
cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.csv')
# Mask LPA Probability Mask and remove NaN
command = [[
'fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'],
'ples_lpa_mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas',
os.path.join(cenc_dirs['wmlesions']['dirs']['input'],
'mask.nii.gz'), '-nan',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_pmap.nii.gz')
]]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Create labels file from LPA probability map
labels.label_connected_components(
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_pmap.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'), lower_threshold,
upper_threshold)
# Measure properties of labels
labels.properties(
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'),
None,
False, ['volume_mm3'],
wm_lesions_stats_filename,
limits_volume_voxels=[min_lesion_volume_voxels, numpy.inf],
limits_bb_volume_voxels=[0, numpy.inf],
limits_fill_factor=[0, 1],
sort='volume_mm3',
verbose=verbose,
verbose_nlines=20)
# Keep labels greater than 10 mm^3. Limit is set above
labels.keep(
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'), [],
wm_lesions_stats_filename,
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'))
# 1) Create a WM Lesions mask
# 2) Mask LPA Registered T2 FLAIR
command = [[
'fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'), '-bin',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_mask.nii.gz')
],
[
'fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'],
'mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas',
os.path.join(cenc_dirs['wmlesions']['dirs']['input'],
'mask.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_t2flair.nii.gz')
]]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Measure Pmaps stats labels
df_pmap_measure = labels.measure(
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_labels.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'],
'wmlesions_lpa_pmap.nii.gz'))
df_pmap_measure.to_csv(wm_lesions_pmap_stats_filename, index=False)
# Write out JSON file with RedCap Instrument
methods_write_json_redcap_instrument(input_dir, wm_lesions_stats_filename,
wm_lesions_pmap_stats_filename,
verbose)
def results(input_dir, verbose):
cenc_dirs = cenc.directories(input_dir)
cenc_freesurfer_dir = cenc_dirs['freesurfer']['mri']
util.mkcd_dir([
cenc_dirs['results']['dirs']['root'],
cenc_dirs['results']['dirs']['images'],
cenc_dirs['results']['dirs']['labels']
], True)
files_to_convert = [
os.path.join(cenc_freesurfer_dir, 'nu.mgz'),
os.path.join(cenc_freesurfer_dir, 'aseg.mgz'),
os.path.join(cenc_freesurfer_dir, 'brainmask.mgz'),
os.path.join(cenc_freesurfer_dir, 'aparc.a2009s+aseg.mgz'),
os.path.join(cenc_freesurfer_dir, 'wmparc.mgz')
]
# Check if files exist
if util.check_files(files_to_convert, True) == False:
sys.exit()
# Create link to directory
if not os.path.exists(cenc_dirs['freesurfer']['results']):
util.force_symbolic_link(cenc_dirs['freesurfer']['mri'],
cenc_dirs['freesurfer']['results'])
# TODO use input node to run this instead of a loop. The trick part is to have the files named correctly.
for ii in files_to_convert:
mc = fs.MRIConvert(in_file=ii,
out_file=os.path.join(
cenc_dirs['results']['dirs']['labels'],
str.replace(os.path.basename(ii), '.mgz',
'.nii.gz')),
out_type='niigz')
mc.run()
reorient = fsl.Reorient2Std(in_file=mc.inputs.out_file,
out_file=mc.inputs.out_file)
reorient.run()
# Link nu.nii.gz to results/native/images
result_files = [[
os.path.join(cenc_dirs['results']['dirs']['labels'], 'nu.nii.gz'),
os.path.join(cenc_dirs['results']['dirs']['images'], 'nu.nii.gz')
]]
for ii in result_files:
util.force_hard_link(ii[0], ii[1])
# Create final brain mask.
cenc.create_mask(
os.path.join(cenc_dirs['results']['dirs']['labels'],
'brainmask.nii.gz'),
os.path.join(cenc_dirs['results']['dirs']['labels'],
'aparc.a2009s+aseg.nii.gz'),
os.path.join(cenc_dirs['results']['dirs']['labels'], 'mask.nii.gz'))
# Create macroscopic labels
labels.extract(os.path.join(cenc_dirs['results']['dirs']['labels'],
'aseg.nii.gz'), [],
[10, 11, 12, 13, 17, 18, 49, 50, 51, 52, 53, 54],
os.path.join(cenc_dirs['results']['dirs']['labels'],
'gm.subcortical.nii.gz'),
merge=1)
labels.extract(os.path.join(cenc_dirs['results']['dirs']['labels'],
'aseg.nii.gz'), [], [3, 42],
os.path.join(cenc_dirs['results']['dirs']['labels'],
'gm.cerebral_cortex.nii.gz'),
merge=1)
labels.extract(os.path.join(cenc_dirs['results']['dirs']['labels'],
'aseg.nii.gz'), [], [8, 47],
os.path.join(cenc_dirs['results']['dirs']['labels'],
'gm.cerebellum.nii.gz'),
merge=1)
labels.extract(os.path.join(cenc_dirs['results']['dirs']['labels'],
'aseg.nii.gz'), [], [2, 41],
os.path.join(cenc_dirs['results']['dirs']['labels'],
'wm.cerebral.nii.gz'),
merge=1)
labels.extract(os.path.join(cenc_dirs['results']['dirs']['labels'],
'aseg.nii.gz'), [], [7, 46],
os.path.join(cenc_dirs['results']['dirs']['labels'],
'wm.cerebellum.nii.gz'),
merge=1)
labels.extract(os.path.join(cenc_dirs['results']['dirs']['labels'],
'aseg.nii.gz'), [], [4, 43],
os.path.join(cenc_dirs['results']['dirs']['labels'],
'ventricles.nii.gz'),
merge=1)
# Brain extraction nu.nii.gz
util.iw_subprocess([
'fslmaths',
os.path.join(cenc_dirs['results']['dirs']['images'], 'nu.nii.gz'),
'-mas',
os.path.join(cenc_dirs['results']['dirs']['labels'], 'mask.nii.gz'),
os.path.join(cenc_dirs['results']['dirs']['images'], 'nu_brain.nii.gz')
])
