from sklearn.model_selection import KFold from torch import nn from torch.cuda.amp import autocast from batchgenerators.utilities.file_and_folder_operations import * from monai.transforms import ( AsDiscreted, AddChanneld, Compose, CropForegroundd, SpatialPadd, ResizeWithPadOrCropd, LoadImaged, Orientationd, RandCropByPosNegLabeld, ScaleIntensityRanged, KeepLargestConnectedComponentd, Spacingd, ToTensord, RandAffined, RandFlipd, RandCropByPosNegLabeld, RandShiftIntensityd, RandRotate90d, EnsureTyped, Invertd, KeepLargestConnectedComponentd, SaveImaged, Activationsd ) import numpy as np from collections import OrderedDict import glob def data_loader(args): root_dir = args.root dataset = args.dataset print('Start to load data from directory: {}'.format(root_dir)) if dataset == 'feta': out_classes = 8 elif dataset == 'flare': out_classes = 5 elif dataset == 'amos': out_classes = 16 if args.mode == 'train': train_samples = {} valid_samples = {} ## Input training data train_img = sorted(glob.glob(os.path.join(root_dir, 'imagesTr', '*.nii.gz'))) train_label = sorted(glob.glob(os.path.join(root_dir, 'labelsTr', '*.nii.gz'))) train_samples['images'] = train_img train_samples['labels'] = train_label ## Input validation data valid_img = sorted(glob.glob(os.path.join(root_dir, 'imagesVal', '*.nii.gz'))) valid_label = sorted(glob.glob(os.path.join(root_dir, 'labelsVal', '*.nii.gz'))) valid_samples['images'] = valid_img valid_samples['labels'] = valid_label print('Finished loading all training samples from dataset: {}!'.format(dataset)) print('Number of classes for segmentation: {}'.format(out_classes)) return train_samples, valid_samples, out_classes elif args.mode == 'test': test_samples = {} ## Input inference data test_img = sorted(glob.glob(os.path.join(root_dir, 'imagesTs', '*.nii.gz'))) test_samples['images'] = test_img print('Finished loading all inference samples from dataset: {}!'.format(dataset)) return test_samples, out_classes def data_transforms(args): dataset = args.dataset if args.mode == 'train': crop_samples = args.crop_sample else: crop_samples = None if dataset == 'feta': train_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), Orientationd(keys=["image", "label"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=0, a_max=1000, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image", "label"], source_key="image"), RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=(96, 96, 96), pos=1, neg=1, num_samples=crop_samples, image_key="image", image_threshold=0, ), RandShiftIntensityd( keys=["image"], offsets=0.10, prob=0.50, ), RandAffined( keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0, spatial_size=(96, 96, 96), rotate_range=(0, 0, np.pi / 15), scale_range=(0.1, 0.1, 0.1)), ToTensord(keys=["image", "label"]), ] ) val_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), Orientationd(keys=["image", "label"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=0, a_max=1000, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image", "label"], source_key="image"), ToTensord(keys=["image", "label"]), ] ) test_transforms = Compose( [ LoadImaged(keys=["image"]), AddChanneld(keys=["image"]), Orientationd(keys=["image"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=0, a_max=1000, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image"], source_key="image"), ToTensord(keys=["image"]), ] ) elif dataset == 'flare': train_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), Spacingd(keys=["image", "label"], pixdim=( 1.0, 1.0, 1.2), mode=("bilinear", "nearest")), # ResizeWithPadOrCropd(keys=["image", "label"], spatial_size=(256,256,128), mode=("constant")), Orientationd(keys=["image", "label"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=-125, a_max=275, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image", "label"], source_key="image"), RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=(96, 96, 96), pos=1, neg=1, num_samples=crop_samples, image_key="image", image_threshold=0, ), RandShiftIntensityd( keys=["image"], offsets=0.10, prob=0.50, ), RandAffined( keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0, spatial_size=(96, 96, 96), rotate_range=(0, 0, np.pi / 30), scale_range=(0.1, 0.1, 0.1)), ToTensord(keys=["image", "label"]), ] ) val_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), Spacingd(keys=["image", "label"], pixdim=( 1.0, 1.0, 1.2), mode=("bilinear", "nearest")), Orientationd(keys=["image", "label"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=-125, a_max=275, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image", "label"], source_key="image"), ToTensord(keys=["image", "label"]), ] ) test_transforms = Compose( [ LoadImaged(keys=["image"]), AddChanneld(keys=["image"]), Spacingd(keys=["image"], pixdim=( 1.0, 1.0, 1.2), mode=("bilinear")), # ResizeWithPadOrCropd(keys=["image"], spatial_size=(168,168,128), mode=("constant")), Orientationd(keys=["image"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=-125, a_max=275, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image"], source_key="image"), ToTensord(keys=["image"]), ] ) elif dataset == 'amos': train_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), Spacingd(keys=["image", "label"], pixdim=( 1.5, 1.5, 2.0), mode=("bilinear", "nearest")), Orientationd(keys=["image", "label"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=-125, a_max=275, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image", "label"], source_key="image"), RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=(96, 96, 96), pos=1, neg=1, num_samples=crop_samples, image_key="image", image_threshold=0, ), RandShiftIntensityd( keys=["image"], offsets=0.10, prob=0.50, ), RandAffined( keys=['image', 'label'], mode=('bilinear', 'nearest'), prob=1.0, spatial_size=(96, 96, 96), rotate_range=(0, 0, np.pi / 30), scale_range=(0.1, 0.1, 0.1)), ToTensord(keys=["image", "label"]), ] ) val_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), Spacingd(keys=["image", "label"], pixdim=( 1.5, 1.5, 2.0), mode=("bilinear", "nearest")), Orientationd(keys=["image", "label"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=-125, a_max=275, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image", "label"], source_key="image"), ToTensord(keys=["image", "label"]), ] ) test_transforms = Compose( [ LoadImaged(keys=["image"]), AddChanneld(keys=["image"]), Spacingd(keys=["image"], pixdim=( 1.5, 1.5, 2.0), mode=("bilinear")), Orientationd(keys=["image"], axcodes="RAS"), ScaleIntensityRanged( keys=["image"], a_min=-125, a_max=275, b_min=0.0, b_max=1.0, clip=True, ), CropForegroundd(keys=["image"], source_key="image"), ToTensord(keys=["image"]), ] ) if args.mode == 'train': print('Cropping {} sub-volumes for training!'.format(str(crop_samples))) print('Performed Data Augmentations for all samples!') return train_transforms, val_transforms elif args.mode == 'test': print('Performed transformations for all samples!') return test_transforms def infer_post_transforms(args, test_transforms, out_classes): post_transforms = Compose([ EnsureTyped(keys="pred"), Activationsd(keys="pred", softmax=True), Invertd( keys="pred", # invert the `pred` data field, also support multiple fields transform=test_transforms, orig_keys="image", # get the previously applied pre_transforms information on the `img` data field, # then invert `pred` based on this information. we can use same info # for multiple fields, also support different orig_keys for different fields meta_keys="pred_meta_dict", # key field to save inverted meta data, every item maps to `keys` orig_meta_keys="image_meta_dict", # get the meta data from `img_meta_dict` field when inverting, # for example, may need the `affine` to invert `Spacingd` transform, # multiple fields can use the same meta data to invert meta_key_postfix="meta_dict", # if `meta_keys=None`, use "{keys}_{meta_key_postfix}" as the meta key, # if `orig_meta_keys=None`, use "{orig_keys}_{meta_key_postfix}", # otherwise, no need this arg during inverting nearest_interp=False, # don't change the interpolation mode to "nearest" when inverting transforms # to ensure a smooth output, then execute `AsDiscreted` transform to_tensor=True, # convert to PyTorch Tensor after inverting ), ## If monai version <= 0.6.0: AsDiscreted(keys="pred", argmax=True, n_classes=out_classes), ## If moani version > 0.6.0: # AsDiscreted(keys="pred", argmax=True) # KeepLargestConnectedComponentd(keys='pred', applied_labels=[1, 3]), SaveImaged(keys="pred", meta_keys="pred_meta_dict", output_dir=args.output, output_postfix="seg", output_ext=".nii.gz", resample=True), ]) return post_transforms