def __init__(self, mode, sub_task='lung', split=0.2, fold=0, n_classes=2, samples=10, dataset_path='../datasets', crop_dim=(32, 32, 32)): print("MANDIBLE SEGMENTATION DATASET") self.CLASSES = n_classes self.fold = int(fold) self.crop_size = crop_dim self.full_vol_dim = (256, 256, 192) # width, height,slice self.mode = mode self.full_volume = None self.affine = None self.list = [] self.samples = samples subvol = '_vol_' + str(crop_dim[0]) + 'x' + str( crop_dim[1]) + 'x' + str(crop_dim[2]) self.sub_vol_path = dataset_path + '/MRBrainS18/subvol_generated/' + mode + subvol + '/' utils.make_dirs(self.sub_vol_path) self.train_images, self.train_labels, self.val_labels, self.val_images = [], [], [], [] list_images = sorted( glob.glob(os.path.join(dataset_path, 'MRBrainS18/images/*'))) list_labels = sorted( glob.glob(os.path.join(dataset_path, 'MRBrainS18/labels/*'))) len_of_data = len(list_images) for i in range(len_of_data): if i >= (self.fold * int(split * len_of_data)) and i < ( (self.fold * int(split * len_of_data)) + int(split * len_of_data)): self.train_images.append(list_images[i]) self.train_labels.append(list_labels[i]) else: self.val_images.append(list_images[i]) self.val_labels.append(list_labels[i]) if (mode == 'train'): self.list_IDs = self.train_images self.list_labels = self.train_labels elif (mode == 'val'): self.list_IDs = self.val_images self.list_labels = self.val_labels self.list = create_sub_volumes(self.list_IDs, self.list_labels, dataset_name='covid19seg', mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path) print("{} SAMPLES = {}".format(mode, len(self.list)))
def __init__(self, args, mode, dataset_path='../datasets', classes=4, dim=(32, 32, 32), split_id=0, samples=1000, load=False): self.mode = mode self.root = dataset_path self.classes = classes dataset_name = 'MICCAI_2020_ribfrac' self.val_path = "/data/chelx/MICCAI-RibFrac2020/dataset/ribfrac-val-images" self.dirs = os.listdir(self.val_path) self.sample = samples self.list = [] self.full_vol_size = (240, 240, 48) self.threshold = args.threshold self.crop_dim = dim self.list_reg_t1 = [] self.labels = [] self.full_volume = None self.save_name = self.root + '/MICCAI_2020_ribfrac/val/classes-' + str( classes) + '-list-' + mode + '-samples-' + str(samples) + '.txt' if load: ## load pre-generated data self.list = utils.load_list(self.save_name) return # subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str(dim[2]) + '_' + str(self.threshold) # subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str(dim[2]) + '_' + str(self.threshold) + '_' + str(args.samples_train) subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str( dim[2]) + '_' + str(self.threshold) + '_overall' self.sub_vol_path = self.root + '/MICCAI_2020_ribfrac/generated/' + mode + subvol + '/' # utils.make_dirs(self.sub_vol_path) if not os.path.exists(self.sub_vol_path): os.makedirs(self.sub_vol_path) if not os.path.exists(os.path.dirname(self.save_name)): os.makedirs(os.path.dirname(self.save_name)) list_reg_t1 = sorted( glob.glob(os.path.join(self.val_path, 'val/*.nii.gz'))) # import ipdb;ipdb.set_trace() labels = sorted( glob.glob(os.path.join(self.val_path, 'val_label_gt/*.nii.gz'))) self.affine = img_loader.load_affine_matrix(list_reg_t1[0]) self.list = create_sub_volumes(list_reg_t1, labels, dataset_name=dataset_name, mode=mode, samples=samples, full_vol_dim=self.full_vol_size, crop_size=self.crop_dim, sub_vol_path=self.sub_vol_path, th_percent=self.threshold) utils.save_list(self.save_name, self.list)
def __init__(self, args, mode, dataset_path='../datasets', classes=4, dim=(32, 32, 32), split_id=0, samples=1000, load=False): self.mode = mode self.root = dataset_path self.classes = classes dataset_name = "mrbrains" + str(classes) self.training_path = self.root + '/mrbrains_2018/training' self.dirs = os.listdir(self.training_path) self.samples = samples self.list = [] self.full_vol_size = (240, 240, 48) self.threshold = 0.1 self.crop_dim = dim self.list_flair = [] self.list_ir = [] self.list_reg_ir = [] self.list_reg_t1 = [] self.labels = [] self.full_volume = None self.save_name = self.root + '/mrbrains_2018/training/mrbrains_2018-classes-' + str( classes) + '-list-' + mode + '-samples-' + str( samples) + '.txt' if load: ## load pre-generated data self.list = utils.load_list(self.save_name) return subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str(dim[2]) self.sub_vol_path = self.root + '/mrbrains_2018/generated/' + mode + subvol + '/' utils.make_dirs(self.sub_vol_path) list_reg_t1 = sorted(glob.glob(os.path.join(self.training_path, '*/pr*/*g_T1.nii.gz'))) list_reg_ir = sorted(glob.glob(os.path.join(self.training_path, '*/pr*/*g_IR.nii.gz'))) list_flair = sorted(glob.glob(os.path.join(self.training_path, '*/pr*/*AIR.nii.gz'))) labels = sorted(glob.glob(os.path.join(self.training_path, '*/*egm.nii.gz'))) self.affine = img_loader.load_affine_matrix(list_reg_t1[0]) split_id = int(split_id) if mode == 'val': labels = [labels[split_id]] list_reg_t1 = [list_reg_t1[split_id]] list_reg_ir = [list_reg_ir[split_id]] list_flair = [list_flair[split_id]] self.full_volume = get_viz_set(list_reg_t1, list_reg_ir, list_flair, labels, dataset_name=dataset_name) else: labels.pop(split_id) list_reg_t1.pop(split_id) list_reg_ir.pop(split_id) list_flair.pop(split_id) self.list = create_sub_volumes(list_reg_t1, list_reg_ir, list_flair, labels, dataset_name=dataset_name, mode=mode, samples=samples, full_vol_dim=self.full_vol_size, crop_size=self.crop_dim, sub_vol_path=self.sub_vol_path, th_percent=self.threshold) utils.save_list(self.save_name, self.list)
def __init__(self, mode, dataset_path='./datasets', crop_dim=(32, 32, 32), split_id=1, samples=1000, load=False): """ :param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param fold_id: 1 to 10 values :param samples: number of sub-volumes that you want to create """ self.mode = mode self.root = str(dataset_path) self.training_path = self.root + '/iseg_2017/iSeg-2017-Training/' self.testing_path = self.root + '/iseg_2017/iSeg-2017-Testing/' self.CLASSES = 4 self.full_vol_dim = (144, 192, 256) # slice, width, height self.crop_size = crop_dim self.list = [] self.samples = samples self.full_volume = None self.save_name = self.root + '/iseg_2017/iSeg-2017-Training/iseg2017-list-' + mode + '-samples-' + str( samples) + '.txt' if load: self.list = utils.load_list(self.save_name) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*T1.img'))) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) return subvol = '_vol_' + str(crop_dim[0]) + 'x' + str( crop_dim[1]) + 'x' + str(crop_dim[2]) self.sub_vol_path = self.root + '/iseg_2017/generated/' + mode + subvol + '/' utils.make_dirs(self.sub_vol_path) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*T1.img'))) list_IDsT2 = sorted( glob.glob(os.path.join(self.training_path, '*T2.img'))) labels = sorted( glob.glob(os.path.join(self.training_path, '*label.img'))) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) if self.mode == 'train': list_IDsT1 = list_IDsT1[:split_id] list_IDsT2 = list_IDsT2[:split_id] labels = labels[:split_id] self.list = create_sub_volumes(list_IDsT1, list_IDsT2, labels, dataset_name="iseg2017", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, threshold=10) elif self.mode == 'val': list_IDsT1 = list_IDsT1[split_id:] list_IDsT2 = list_IDsT2[:split_id:] labels = labels[split_id:] self.list = create_sub_volumes(list_IDsT1, list_IDsT2, labels, dataset_name="iseg2017", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, threshold=10) self.full_volume = get_viz_set(list_IDsT1, list_IDsT2, labels) elif self.mode == 'test': self.list_IDsT1 = sorted( glob.glob(os.path.join(self.testing_path, '*T1.img'))) self.list_IDsT2 = sorted( glob.glob(os.path.join(self.testing_path, '*T2.img'))) self.labels = None utils.save_list(self.save_name, self.list)
def __init__(self, args, mode, dataset_path='./datasets', classes=5, crop_dim=(32, 32, 32), split_idx=10, samples=10, load=False): """ :param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param split_idx: 1 to 10 values :param samples: number of sub-volumes that you want to create """ self.mode = mode self.root = str(dataset_path) self.training_path = self.root + '/MICCAI_BraTS_2018_Data_Training/' self.testing_path = self.root + ' ' self.CLASSES = 4 self.full_vol_dim = (240, 240, 155) # slice, width, height self.crop_size = crop_dim self.threshold = args.threshold self.normalization = args.normalization self.augmentation = args.augmentation self.list = [] self.samples = samples self.full_volume = None self.classes = classes self.save_name = self.root + '/MICCAI_BraTS_2018_Data_Training/brats2018-list-' + mode + '-samples-' + str( samples) + '.txt' if self.augmentation: self.transform = augment3D.RandomChoice(transforms=[ augment3D.GaussianNoise(mean=0, std=0.01), augment3D.RandomFlip(), augment3D.ElasticTransform() ], p=0.5) if load: ## load pre-generated data list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz'))) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) self.list = utils.load_list(self.save_name) return subvol = '_vol_' + str(crop_dim[0]) + 'x' + str( crop_dim[1]) + 'x' + str(crop_dim[2]) self.sub_vol_path = self.root + '/MICCAI_BraTS_2018_Data_Training/generated/' + mode + subvol + '/' utils.make_dirs(self.sub_vol_path) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz'))) list_IDsT1ce = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t1ce.nii.gz'))) list_IDsT2 = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t2.nii.gz'))) list_IDsFlair = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*_flair.nii.gz'))) labels = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*_seg.nii.gz'))) # print(len(list_IDsT1),len(list_IDsT2),len(list_IDsFlair),len(labels)) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) if self.mode == 'train': list_IDsT1 = list_IDsT1[:split_idx] list_IDsT1ce = list_IDsT1ce[:split_idx] list_IDsT2 = list_IDsT2[:split_idx] list_IDsFlair = list_IDsFlair[:split_idx] labels = labels[:split_idx] self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, dataset_name="brats2018", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, normalization=self.normalization, th_percent=self.threshold) elif self.mode == 'val': list_IDsT1 = list_IDsT1[split_idx:] list_IDsT1ce = list_IDsT1ce[split_idx:] list_IDsT2 = list_IDsT2[split_idx:] list_IDsFlair = list_IDsFlair[split_idx:] labels = labels[split_idx:] self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, dataset_name="brats2018", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, normalization=self.normalization, th_percent=self.threshold) elif self.mode == 'test': self.list_IDsT1 = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz'))) self.list_IDsT1ce = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t1ce.nii.gz'))) self.list_IDsT2 = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz'))) self.list_IDsFlair = sorted( glob.glob( os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz'))) self.labels = None utils.save_list(self.save_name, self.list)
def __init__(self, args, mode, dataset_path='./datasets', crop_dim=(32, 32, 32), split_id=1, samples=1000, load=False): """ :param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param fold_id: 1 to 10 values :param samples: number of sub-volumes that you want to create """ self.mode = mode self.root = str(dataset_path) self.training_path = self.root + '/iseg_2019/iSeg-2019-Training/' self.testing_path = self.root + '/iseg_2019/iSeg-2019-Validation/' self.CLASSES = 4 self.full_vol_dim = (144, 192, 256) # slice, width, height self.crop_size = crop_dim self.threshold = args.threshold self.normalization = args.normalization self.augmentation = args.augmentation self.list = [] self.samples = samples self.full_volume = None self.save_name = self.root + '/iseg_2019/iseg2019-list-' + mode + '-samples-' + str( samples) + '.txt' if self.augmentation: self.transform = augment3D.RandomChoice(transforms=[ augment3D.GaussianNoise(mean=0, std=0.01), augment3D.RandomFlip(), augment3D.ElasticTransform() ], p=0.5) if load: ## load pre-generated data self.list = utils.load_list(self.save_name) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*T1.img'))) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) return subvol = '_vol_' + str(crop_dim[0]) + 'x' + str( crop_dim[1]) + 'x' + str(crop_dim[2]) self.sub_vol_path = self.root + '/iseg_2019/generated/' + mode + subvol + '/' utils.make_dirs(self.sub_vol_path) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*T1.img'))) list_IDsT2 = sorted( glob.glob(os.path.join(self.training_path, '*T2.img'))) labels = sorted( glob.glob(os.path.join(self.training_path, '*label.img'))) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) if self.mode == 'train': list_IDsT1 = list_IDsT1[:split_id] list_IDsT2 = list_IDsT2[:split_id] labels = labels[:split_id] self.list = create_sub_volumes(list_IDsT1, list_IDsT2, labels, dataset_name="iseg2019", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, th_percent=self.threshold) elif self.mode == 'val': list_IDsT1 = list_IDsT1[split_id:] list_IDsT2 = list_IDsT2[:split_id:] labels = labels[split_id:] self.list = create_sub_volumes(list_IDsT1, list_IDsT2, labels, dataset_name="iseg2019", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, th_percent=self.threshold) self.full_volume = get_viz_set(list_IDsT1, list_IDsT2, labels, dataset_name="iseg2019") elif self.mode == 'test': self.list_IDsT1 = sorted( glob.glob(os.path.join(self.testing_path, '*T1.img'))) self.list_IDsT2 = sorted( glob.glob(os.path.join(self.testing_path, '*T2.img'))) self.labels = None # todo inference here utils.save_list(self.save_name, self.list)
def __init__(self, args, mode, dataset_path='../../datasets', classes=4, crop_dim=(128, 128, 128), split_idx=204, samples=10, load=False): """ :param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param split_idx: 1 to 10 values :param samples: number of sub-volumes that you want to create """ self.mode = mode self.root = str(dataset_path) self.training_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/train_generated' self.testing_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Validation/' self.full_vol_dim = (128, 128, 128) # slice, width, height self.crop_size = crop_dim self.threshold = args.threshold self.normalization = args.normalization self.augmentation = args.augmentation self.list = [] self.samples = samples self.full_volume = None self.classes = classes if self.augmentation: self.transform = augment3D.RandomChoice(transforms=[ augment3D.GaussianNoise(mean=0, std=0.01), augment3D.RandomFlip(), augment3D.ElasticTransform() ], p=0.5) self.save_name = self.root + '/brats2019/brats2019-list-' + mode + '-samples-' + str( samples) + '.txt' if load: ## load pre-generated data self.list = utils.load_list(self.save_name) list_IDsT1 = sorted( glob.glob( os.path.join(self.training_path, '***_imgs/***_imgs_t1.npy'))) # self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) return subvol = '_vol_' + str(crop_dim[0]) + 'x' + str( crop_dim[1]) + 'x' + str(crop_dim[2]) self.sub_vol_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/generated/' + mode + subvol + '/' # if not os.path.exists(self.sub_vol_path): # utils.make_dirs(self.sub_vol_path) list_IDsT1 = sorted( glob.glob( os.path.join(self.training_path, '***_imgs/***_imgs_t1.npy'))) list_IDsT1ce = sorted( glob.glob( os.path.join(self.training_path, '***_imgs/***_imgs_t1gd.npy'))) list_IDsT2 = sorted( glob.glob( os.path.join(self.training_path, '***_imgs/***_imgs_t2.npy'))) list_IDsFlair = sorted( glob.glob( os.path.join(self.training_path, '***_imgs/***_imgs_flair.npy'))) labels = sorted( glob.glob( os.path.join(self.training_path, '***_seg/***_seg_label.npy'))) # list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels = utils.shuffle_lists(list_IDsT1, list_IDsT1ce, # list_IDsT2, # list_IDsFlair, labels, # seed=17) self.affine = np.diag([1, 1, 1, 1]) if self.mode == 'train': list_IDsT1 = list_IDsT1[:split_idx] list_IDsT1ce = list_IDsT1ce[:split_idx] list_IDsT2 = list_IDsT2[:split_idx] list_IDsFlair = list_IDsFlair[:split_idx] labels = labels[:split_idx] # self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, # dataset_name="brats2019", mode=mode, samples=samples, # full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, # sub_vol_path=self.sub_vol_path, th_percent=self.threshold) # with open(self.save_name, "wb") as fp: # pickle.dump(self.list,fp) with open(self.save_name, "rb") as fp: self.list = pickle.load(fp) elif self.mode == 'val': list_IDsT1 = list_IDsT1[split_idx:] list_IDsT1ce = list_IDsT1ce[split_idx:] list_IDsT2 = list_IDsT2[split_idx:] list_IDsFlair = list_IDsFlair[split_idx:] labels = labels[split_idx:] self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, dataset_name="brats2019", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path, th_percent=self.threshold) with open(self.save_name, "wb") as fp: pickle.dump(self.list, fp) # with open(self.save_name, "rb") as fp: # self.list = pickle.load(fp) elif self.mode == 'test': self.list_IDsT1 = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz'))) self.list_IDsT1ce = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t1ce.nii.gz'))) self.list_IDsT2 = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz'))) self.list_IDsFlair = sorted( glob.glob( os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz'))) self.labels = None
def __init__(self, mode, dataset_path='./datasets', classes=5, crop_dim=(200, 200, 150), split_idx=260, samples=10, load=False): """ :param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param split_idx: 1 to 10 values :param samples: number of sub-volumes that you want to create """ self.mode = mode self.root = str(dataset_path) self.training_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/' self.testing_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Validation/' self.full_vol_dim = (240, 240, 155) # slice, width, height self.crop_size = crop_dim self.list = [] self.samples = samples self.full_volume = None self.classes = classes self.save_name = self.root + '/brats2019/brats2019-list-' + mode + '-samples-' + str( samples) + '.txt' if load: self.list = utils.load_list(self.save_name) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz'))) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) return subvol = '_vol_' + str(crop_dim[0]) + 'x' + str( crop_dim[1]) + 'x' + str(crop_dim[2]) self.sub_vol_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Training/generated/' + mode + subvol + '/' utils.make_dirs(self.sub_vol_path) list_IDsT1 = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz'))) list_IDsT1ce = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t1ce.nii.gz'))) list_IDsT2 = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*t2.nii.gz'))) list_IDsFlair = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*_flair.nii.gz'))) labels = sorted( glob.glob(os.path.join(self.training_path, '*GG/*/*_seg.nii.gz'))) list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels = utils.shuffle_lists( list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, seed=17) self.affine = img_loader.load_affine_matrix(list_IDsT1[0]) if self.mode == 'train': print('Brats2019, Total data:', len(list_IDsT1)) list_IDsT1 = list_IDsT1[:split_idx] list_IDsT1ce = list_IDsT1ce[:split_idx] list_IDsT2 = list_IDsT2[:split_idx] list_IDsFlair = list_IDsFlair[:split_idx] labels = labels[:split_idx] self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, dataset_name="brats2019", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path) elif self.mode == 'val': list_IDsT1 = list_IDsT1[split_idx:] list_IDsT1ce = list_IDsT1ce[split_idx:] list_IDsT2 = list_IDsT2[split_idx:] list_IDsFlair = list_IDsFlair[split_idx:] labels = labels[split_idx:] self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels, dataset_name="brats2019", mode=mode, samples=samples, full_vol_dim=self.full_vol_dim, crop_size=self.crop_size, sub_vol_path=self.sub_vol_path) elif self.mode == 'test': self.list_IDsT1 = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz'))) self.list_IDsT1ce = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t1ce.nii.gz'))) self.list_IDsT2 = sorted( glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz'))) self.list_IDsFlair = sorted( glob.glob( os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz'))) self.labels = None # Todo inference code here utils.save_list(self.save_name, self.list)