import os, glob from .utils import * augmentation_funcs = { 'gamma': add_gamma_transform, 'bias_field': add_bias_field, 'resample': resample_resolution, 'noise': add_noise, } processing_funcs = { 'T1': read_and_deform_image, 'T2': read_and_deform_image, 'FLAIR': read_and_deform_image, 'CT': read_and_deform_CT, 'segmentation': read_and_deform_segmentation, 'surface': read_and_deform_surface, 'distance': read_and_deform_distance, 'bias_field': read_and_deform_bias_field, 'registration': read_and_deform_registration, 'pathology': read_and_deform_pathology, } dataset_setups = { 'ADHD': { 'root': '/autofs/space/yogurt_001/users/pl629/data/adhd200_crop', 'pathology_type': None, 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': 'T1', 'T2': None, 'FLAIR': None, 'CT': None, # processed ground truths 'surface': None, #'surfaces', TODO 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': None, 'pathology_prob': None, } }, 'HCP': { 'root': '/autofs/space/yogurt_001/users/pl629/data/hcp_crop', 'pathology_type': None, 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1', 'T2'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': 'T1', 'T2': 'T2', 'FLAIR': None, 'CT': None, # processed ground truths 'surface': None, #'surfaces', 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': None, 'pathology_prob': None, } }, 'AIBL': { 'root': '/autofs/space/yogurt_001/users/pl629/data/aibl_crop', 'pathology_type': None, 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1', 'T2', 'FLAIR'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': 'T1', 'T2': 'T2', 'FLAIR': 'FLAIR', 'CT': None, # processed ground truths 'surface': None, #'surfaces', 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': None, 'pathology_prob': None, } }, 'OASIS': { 'root': '/autofs/space/yogurt_001/users/pl629/data/oasis3', 'pathology_type': None, 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1', 'CT'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': 'T1', 'T2': None, 'FLAIR': None, 'CT': 'CT', # processed ground truths 'surface': None, #'surfaces', 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': None, 'pathology_prob': None, } }, 'ADNI': { 'root': '/autofs/space/yogurt_001/users/pl629/data/adni_crop', 'pathology_type': None, #'wmh', 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': 'Dmaps', 'DmapsBag': 'DmapsBag', # real images 'T1': 'T1', 'T2': None, 'FLAIR': None, 'CT': None, # processed ground truths 'surface': 'surfaces', 'distance': 'Dmaps', 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': 'pathology_maps_segmentation', 'pathology_prob': 'pathology_probability', } }, 'ADNI3': { 'root': '/autofs/space/yogurt_001/users/pl629/data/adni3_crop', 'pathology_type': None, # 'wmh', 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1', 'FLAIR'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': 'T1', 'T2': None, 'FLAIR': 'FLAIR', 'CT': None, # processed ground truths 'surface': None, #'surfaces', TODO 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': 'pathology_maps_segmentation', 'pathology_prob': 'pathology_probability', } }, 'ATLAS': { 'root': '/autofs/space/yogurt_001/users/pl629/data/atlas_crop', 'pathology_type': 'stroke', 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['T1'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': 'T1', 'T2': None, 'FLAIR': None, 'CT': None, # processed ground truths 'surface': None, #'surfaces', TODO 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': 'pathology_maps_segmentation', 'pathology_prob': 'pathology_probability', } }, 'ISLES': { 'root': '/autofs/space/yogurt_001/users/pl629/data/isles2022_crop', 'pathology_type': 'stroke', 'train': 'train.txt', 'test': 'test.txt', 'modalities': ['FLAIR'], 'paths':{ # for synth 'Gen': 'label_maps_generation', 'Dmaps': None, 'DmapsBag': None, # real images 'T1': None, 'T2': None, 'FLAIR': 'FLAIR', 'CT': None, # processed ground truths 'surface': None, #'surfaces', TODO 'distance': None, 'segmentation': 'label_maps_segmentation', 'bias_field': None, 'pathology': 'pathology_maps_segmentation', 'pathology_prob': 'pathology_probability', } }, } all_dataset_names = dataset_setups.keys() # get all pathologies pathology_paths = [] pathology_prob_paths = [] for name, dict in dataset_setups.items(): # TODO: select what kind of shapes? if dict['paths']['pathology'] is not None and dict['pathology_type'] is not None and dict['pathology_type'] == 'stroke': pathology_paths += glob.glob(os.path.join(dict['root'], dict['paths']['pathology'], '*.nii.gz')) \ + glob.glob(os.path.join(dict['root'], dict['paths']['pathology'], '*.nii')) pathology_prob_paths += glob.glob(os.path.join(dict['root'], dict['paths']['pathology_prob'], '*.nii.gz')) \ + glob.glob(os.path.join(dict['root'], dict['paths']['pathology_prob'], '*.nii')) n_pathology = len(pathology_paths) # with csf # NOTE old version (FreeSurfer standard), non-vast label_list_segmentation = [0,14,15,16,24,77,85, 2, 3, 4, 7, 8, 10,11,12,13,17,18,26,28, 41,42,43,46,47,49,50,51,52,53,54,58,60] # 33 n_neutral_labels = 7 ## NEW VAST synth label_list_segmentation_brainseg_with_extracerebral = [0, 11, 12, 13, 16, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 17, 47, 49, 51, 53, 55, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 48, 50, 52, 54, 56] n_neutral_labels_brainseg_with_extracerebral = 20 label_list_segmentation_brainseg_left = [0, 1, 2, 3, 4, 7, 8, 9, 10, 14, 15, 17, 31, 34, 36, 38, 40, 42]