def run_deeid():
args = get_parser().parse_args()
subjects_to_analyze = []
# special variable set in the container
if os.getenv('IS_DOCKER'):
exec_env = 'singularity'
cgroup = Path('/proc/1/cgroup')
if cgroup.exists() and 'docker' in cgroup.read_text():
exec_env = 'docker'
else:
exec_env = 'local'
if args.brainextraction is None:
raise Exception(
"For post defacing quality it is required to run a form of brainextraction"
"on the non-deindentified data. Thus please either indicate bet "
"(--brainextration bet) or nobrainer (--brainextraction nobrainer)."
)
if args.skip_bids_validation:
print("Input data will not be checked for BIDS compliance.")
else:
print("Making sure the input data is BIDS compliant "
"(warnings can be ignored in most cases).")
validate_input_dir(exec_env, args.bids_dir, args.participant_label)
layout = BIDSLayout(args.bids_dir)
if args.analysis_level == "participant":
if args.participant_label:
subjects_to_analyze = args.participant_label
else:
print("No participant label indicated. Please do so.")
else:
subjects_to_analyze = layout.get(return_type='id', target='subject')
list_part_prob = []
for part in subjects_to_analyze:
if part not in layout.get_subjects():
list_part_prob.append(part)
if len(list_part_prob) >= 1:
raise Exception(
"The participant(s) you indicated are not present in the BIDS dataset, please check again."
"This refers to:")
print(list_part_prob)
sessions_to_analyze = layout.get(return_type='id', target='session')
if not sessions_to_analyze:
print('Processing data from one session.')
else:
print('Processing data from %s sessions:' %
str(len(sessions_to_analyze)))
print(sessions_to_analyze)
list_check_meta = args.check_meta
list_field_del = args.del_meta
for subject_label in subjects_to_analyze:
if not sessions_to_analyze:
list_t1w = layout.get(subject=subject_label,
extension='nii.gz',
suffix='T1w',
return_type='filename')
else:
list_t1w = layout.get(subject=subject_label,
extension='nii.gz',
suffix='T1w',
return_type='filename',
session=sessions_to_analyze)
for T1_file in list_t1w:
check_outpath(args.bids_dir, subject_label)
if args.brainextraction == 'bet':
if args.bet_frac is None:
raise Exception(
"If you want to use BET for pre-defacing brain extraction,"
"please provide a Frac value. For example: --bet_frac 0.5"
)
else:
run_brain_extraction_bet(T1_file, args.bet_frac[0],
subject_label, args.bids_dir)
elif args.brainextraction == 'nobrainer':
run_brain_extraction_nb(T1_file, subject_label, args.bids_dir)
check_meta_data(args.bids_dir, subject_label, list_check_meta)
source_t1w = copy_no_deid(args.bids_dir, subject_label, T1_file)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label, list_field_del)
if args.deid == "pydeface":
run_pydeface(source_t1w, T1_file)
elif args.deid == "mri_deface":
run_mri_deface(source_t1w, T1_file)
elif args.deid == "quickshear":
run_quickshear(source_t1w, T1_file)
elif args.deid == "mridefacer":
run_mridefacer(source_t1w, T1_file)
elif args.deid == "deepdefacer":
run_deepdefacer(source_t1w, subject_label, args.bids_dir)
if args.deface_t2w:
if not sessions_to_analyze:
list_t2w = layout.get(subject=subject_label,
extension='nii.gz',
suffix='T2w',
return_type='filename')
else:
list_t2w = layout.get(subject=subject_label,
extension='nii.gz',
suffix='T2w',
return_type='filename',
session=sessions_to_analyze)
if list_t2w == []:
raise Exception(
"You indicated that a T2w image should be defaced as well."
"However, no T2w image exists for subject %s."
"Please check again." % subject_label)
for T2_file in list_t2w:
if args.brainextraction == 'bet':
run_brain_extraction_bet(T2_file, args.bet_frac[0],
subject_label, args.bids_dir)
elif args.brainextraction == 'nobrainer':
run_brain_extraction_nb(T2_file, subject_label,
args.bids_dir)
source_t2w = copy_no_deid(args.bids_dir, subject_label,
T2_file)
run_t2w_deface(source_t2w, T1_file, T2_file)
rename_non_deid(args.bids_dir, subject_label)
if not sessions_to_analyze and args.deface_t2w is False:
create_graphics(args.bids_dir,
subject_label,
session=None,
t2w=None)
elif sessions_to_analyze and args.deface_t2w is False:
for session in sessions_to_analyze:
create_graphics(args.bids_dir,
subject_label,
session=session,
t2w=None)
elif not sessions_to_analyze and args.deface_t2w:
create_graphics(args.bids_dir,
subject_label,
session=None,
t2w=True)
elif sessions_to_analyze and args.deface_t2w:
for session in sessions_to_analyze:
create_graphics(args.bids_dir,
subject_label,
session=session,
t2w=True)
if not sessions_to_analyze:
clean_up_files(args.bids_dir, subject_label)
else:
for session in sessions_to_analyze:
clean_up_files(args.bids_dir, subject_label, session=session)
def run_deeid():
args = get_parser().parse_args()
subjects_to_analyze = []
# special variable set in the container
if os.getenv('IS_DOCKER'):
exec_env = 'singularity'
cgroup = Path('/proc/1/cgroup')
if cgroup.exists() and 'docker' in cgroup.read_text():
exec_env = 'docker'
# print("Making sure the input data is BIDS compliant "
# "(warnings can be ignored in most cases).")
# validate_input_dir(exec_env, args.bids_dir, args.participant_label)
if args.analysis_level == "participant":
if args.participant_label:
subjects_to_analyze = args.participant_label
else:
print("No participant label indicated. Please do so.")
else:
subject_dirs = glob(os.path.join(args.bids_dir, "sub-*"))
subjects_to_analyze = [
subject_dir.split("-")[-1] for subject_dir in subject_dirs
]
list_check_meta = args.check_meta
list_field_del = args.del_meta
modality = "T1w"
if args.deface_t2w:
modality = "T2w"
for subject_label in subjects_to_analyze:
for T1_file in glob(os.path.join(args.bids_dir, "sub-%s" % subject_label,
"anat", "*_"+modality+".nii*")) + \
glob(os.path.join(args.bids_dir, "sub-%s" % subject_label,
"ses-*", "anat", "*_"+modality+".nii*")):
check_outpath(args.bids_dir, subject_label)
if args.brainextraction == 'bet':
if args.bet_frac is None:
raise Exception(
'If you want to use BET for pre-defacing brain extraction, \
please provide a Frac value. For example: --bet_frac 0.5')
else:
run_brain_extraction_bet(T1_file, args.bet_frac[0],
subject_label, args.bids_dir)
elif args.brainextraction == 'nobrainer':
run_brain_extraction_nb(T1_file, subject_label, args.bids_dir)
if args.deid == "pydeface":
if args.del_nodeface == "del":
run_pydeface(T1_file, T1_file)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
else:
#copy_no_deid(subject_label, args.bids_dir, T1_file)
T1_file_masked = T1_file[:-10] + "mod-" + modality + "_defacemask.nii.gz"
run_pydeface(T1_file, T1_file_masked)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
if args.deid == "mri_deface":
if args.del_nodeface == "del":
run_mri_deface(T1_file, T1_file)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
else:
# copy_no_deid(subject_label, args.bids_dir, T1_file)
T1_file_masked = T1_file[:-10] + "mod-" + modality + "_defacemask.nii.gz"
run_mri_deface(T1_file, T1_file_masked)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
if args.deid == "quickshear":
if args.del_nodeface == "del":
run_quickshear(T1_file, T1_file)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
else:
#copy_no_deid(subject_label, args.bids_dir, T1_file)
T1_file_masked = T1_file[:-10] + "mod-" + modality + "_defacemask.nii.gz"
run_quickshear(T1_file, T1_file_masked)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
if args.deid == "mridefacer":
if args.del_nodeface == "del":
run_mridefacer(T1_file, subject_label, args.bids_dir, True,
modality)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
else:
#copy_no_deid(subject_label, args.bids_dir, T1_file)
run_mridefacer(T1_file, subject_label, args.bids_dir,
False, modality)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
if args.deid == "deepdefacer":
if args.del_nodeface == "del":
run_deepdefacer(T1_file, subject_label, args.bids_dir,
True, modality)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)
else:
#copy_no_deid(subject_label, args.bids_dir, T1_file)
run_deepdefacer(T1_file, subject_label, args.bids_dir,
False, modality)
check_meta_data(args.bids_dir, subject_label,
list_check_meta)
print(T1_file)
if args.del_meta:
del_meta_data(args.bids_dir, subject_label,
list_field_del)