subjects = pd.read_csv('/scr/ilz3/myelinconnect/subjects.csv')
subjects=list(subjects['DB'])
subjects.remove('KSMT')
labels= [11, 12, 13, 16, 18] + range(30,42)
templates={'seg': '/scr/ilz3/myelinconnect/struct/seg/{subject}*seg_merged.nii.gz'}
mask_file = '/scr/ilz3/myelinconnect/struct/myelinated_thickness/subcortex_mask/%s_subcortical_mask.nii.gz'
for subject in subjects:
select = SelectFiles(templates)
select.inputs.subject = subject
select.run()
seg_file = select.aggregate_outputs().seg
binarize = Binarize(match = labels,
out_type = 'nii.gz')
binarize.inputs.binary_file = mask_file%subject
binarize.inputs.in_file=seg_file
binarize.run()
