import monai import cv2 from monai.transforms import MapTransform import math import numpy as np import torch import morphsnakes as ms import monai import nrrd import torchvision.transforms as transforms from monai.transforms import ( Activations, AsDiscreteD, AsDiscrete, Compose, CastToTypeD, RandSpatialCropd, ToTensorD, CropForegroundD, Resized, GaussianSmoothD, LoadImageD, TransposeD, OrientationD, ScaleIntensityRangeD, RandAffineD, ResizeWithPadOrCropd, ToTensor, FillHoles, KeepLargestConnectedComponent, HistogramNormalizeD, NormalizeIntensityD ) def define_transforms_loadonly(): transformations = Compose([ LoadImageD(keys=["mask"], reader="NrrdReader", ensure_channel_first=True), ConvertMaskValues(keys=["mask"], keep_classes=["liver", "tumor"]), ToTensor() ]) return transformations def define_post_processing(config): # Post-processing transforms post_processing = [ # Apply softmax activation to convert logits to probabilities Activations(sigmoid=True), # Convert predicted probabilities to discrete values (0 or 1) AsDiscrete(argmax=True, to_onehot=None if len(config['KEEP_CLASSES']) <= 2 else len(config['KEEP_CLASSES'])), # Remove small connected components for 1=liver and 2=tumor KeepLargestConnectedComponent(applied_labels=[1]), # Fill holes in the binary mask for 1=liver and 2=tumor FillHoles(applied_labels=[1]), ToTensor() ] return Compose(post_processing) def define_transforms(config): transformations_test = [ LoadImageD(keys=["image", "mask"], reader="NrrdReader", ensure_channel_first=True), # Orient up and down OrientationD(keys=["image", "mask"], axcodes="PLI"), ToTensorD(keys=["image", "mask"]) # histogram equilization or normalization # HistogramNormalizeD(keys=["image"], num_bins=256, min=0, max=1), # Intensity normalization # NormalizeIntensityD(keys=["image"]), #CastToTypeD(keys=["image"], dtype=torch.float32), #CastToTypeD(keys=["mask"], dtype=torch.int32), ] if config['MASKNONLIVER']: transformations_test.extend( [ MaskOutNonliver(mask_key="mask"), CropForegroundD(keys=["image", "mask"], source_key="image", allow_smaller=True), ] ) transformations_test.append( # Windowing based on liver parameters ScaleIntensityRangeD(keys=["image"], a_min=config['HU_RANGE'][0], a_max=config['HU_RANGE'][1], b_min=0.0, b_max=1.0, clip=True ) ) if config['PREPROCESSING'] == "clihe": transformations_test.append(CLIHE(keys=["image"])) elif config['PREPROCESSING'] == "gaussian": transformations_test.append(GaussianSmoothD(keys=["image"], sigma=0.5)) # convert labels to 0,1,2 instead of 0,1,2,3,4 transformations_test.append(ConvertMaskValues(keys=["mask"], keep_classes=config['KEEP_CLASSES'])) if len(config['KEEP_CLASSES']) > 2: # NEEDED FOR MULTICLASS https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb transformations_test.append(AsDiscreteD(keys=["mask"], to_onehot=len(config['KEEP_CLASSES']))) # (N, C, H, W) 2d; (1, C, H, W, Z) if "3D" not in config['MODEL_NAME']: transformations_test.append(TransposeD(keys=["image", "mask"], indices=(3,0,1,2))) # training transforms include data augmentation transformations_train = transformations_test.copy() if config['MASKNONLIVER']: transformations_test = transformations_test[:4] + transformations_test[5:] # do not crop to liver foregroudn if config['DATA_AUGMENTATION']: if "3D" in config["MODEL_NAME"]: transformations_train.append( RandAffineD(keys=["image", "mask"], prob=0.2, padding_mode="border", mode="bilinear", spatial_size=config['ROI_SIZE'], rotate_range=(0.15,0.15,0.15), #translate_range=(30,30,30), scale_range=(0.1,0.1,0.1))) else: transformations_train.append( RandAffineD(keys=["image", "mask"], prob=0.2, padding_mode="border", mode="bilinear", #spatial_size=(512, 512), rotate_range=(0.15,0.15), #translate_range=(30,30), scale_range=(0.1,0.1))) transformations_train.extend( [ RandSpatialCropd(keys=["image", "mask"], roi_size=config['ROI_SIZE'], random_size=False), ResizeWithPadOrCropd(keys=["image", "mask"], spatial_size=config['ROI_SIZE'], method="end", mode='constant', value=0) ] ) postprocessing_transforms = define_post_processing(config) preprocessing_transforms_test = Compose(transformations_test) preprocessing_transforms_train = Compose(transformations_train) preprocessing_transforms_train.set_random_state(seed=1) preprocessing_transforms_test.set_random_state(seed=1) return preprocessing_transforms_train, preprocessing_transforms_test, postprocessing_transforms class CLIHE(MapTransform): def __init__(self, keys, allow_missing_keys=False): super().__init__(allow_missing_keys) self.keys = keys def __call__(self, data): for key in self.keys: if len(data['image'].shape) > 3: # 3D image data[key] = self.apply_clahe_3d(data[key]) # [B, 1, H, W, Z] else: data[key] = self.apply_clahe_2d(data[key]) # [B, 1, H, W, Z] return data def apply_clahe_3d(self, image): image = np.asarray(image) clahe_slices = [] for slice_idx in range(image.shape[-1]): # Extract the current slice slice_2d = image[0, :, :, slice_idx] # Apply CLAHE to the current slice # slice_2d = cv2.medianBlur(slice_2d, 5) # slice_2d = cv2.anisotropicDiffusion(slice_2d, alpha=0.1, K=1, iterations=50) # slice_2d = anisotropic_diffusion(slice_2d) # slice_2d = cv2.Sobel(slice_2d, cv2.CV_64F, dx=1, dy=1, ksize=5) clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(16,16)) slice_2d = clahe.apply(slice_2d.astype(np.uint8)) #cv2.threshold(clahe_slice, 155, 255, cv2.THRESH_BINARY) kernel = np.ones((2,2), np.float32)/4 slice_2d = cv2.filter2D(slice_2d, -1, kernel) #t = anisodiff2D(delta_t=0.2,kappa=50) #slice_2d = t.fit(slice_2d) # Append the CLAHE enhanced slice to the list clahe_slices.append(slice_2d) # Stack the CLAHE enhanced slices along the slice axis to form the 3D image clahe_image = np.stack(clahe_slices, axis=-1) return torch.from_numpy(clahe_image[None,:]) def apply_clahe_2d(self, image): image = np.asarray(image) clahe = cv2.createCLAHE(clipLimit=5) clahe_slice = clahe.apply(image[0].astype(np.uint8)) return torch.from_numpy(clahe_slice) class GaussianFilter(MapTransform): def __init__(self, keys, allow_missing_keys=False): super().__init__(allow_missing_keys) self.keys = keys def __call__(self, data): for key in self.keys: if len(data['image'].shape) > 3: # 3D image data[key] = self.apply_clahe_3d(data[key]) # [B, 1, H, W, Z] else: data[key] = self.apply_clahe_2d(data[key]) # [B, 1, H, W, Z] return data def apply_clahe_3d(self, image): image = np.asarray(image) clahe_slices = [] for slice_idx in range(image.shape[-1]): # Extract the current slice slice_2d = image[0, :, :, slice_idx] # Apply CLAHE to the current slice kernel = np.ones((3,3), np.float32)/9 slice_2d = cv2.filter2D(slice_2d, -1, kernel) # Append the CLAHE enhanced slice to the list clahe_slices.append(slice_2d) # Stack the CLAHE enhanced slices along the slice axis to form the 3D image clahe_image = np.stack(clahe_slices, axis=-1) return torch.from_numpy(clahe_image[None,:]) def apply_clahe_2d(self, image): image = np.asarray(image) kernel = np.ones((3,3), np.float32)/9 slice_2d = cv2.filter2D(image, -1, kernel) return torch.from_numpy(slice_2d) class Morphsnakes(MapTransform): # https://github.com/pmneila/morphsnakes/blob/master/morphsnakes.py def __init__(self, allow_missing_keys=False): super().__init__(allow_missing_keys) def __call__(self, data): if np.sum(data['mask'][-1]) > 0: res = ms.morphological_chan_vese(data['image'][0], iterations=2, init_level_set=data['mask'][-1]) data['mask'] = res return data class MaskOutNonliver(MapTransform): def __init__(self, allow_missing_keys=False, mask_key="mask"): super().__init__(allow_missing_keys) self.mask_key = mask_key def __call__(self, data): # mask out non-liver regions of an image # non-liver regions are liver, tumor, or portal vein if data[self.mask_key].shape != data['image'].shape: return data data['image'][data[self.mask_key] >= 4] = -1000 data['image'][data[self.mask_key] <= 0] = -1000 return data class ConvertMaskValues(MapTransform): def __init__(self, keys, allow_missing_keys=False, keep_classes=["normal", "liver", "tumor"]): super().__init__(keys, allow_missing_keys) self.keep_classes = keep_classes def __call__(self, data): # original labels: 0 for normal region, 1 for liver, 2 for tumor mass, 3 for portal vein, and 4 for abdominal aorta. # converted labels: 0 for normal region and abdominal aorta, 1 for liver and portal vein, 2 for tumor mass for key in self.keys: data[key][data[key] > 4] = 4 # one patient had class label = 5, converted to 4 if key in data: if "liver" not in self.keep_classes: data[key][data[key] == 1] = 0 if "tumor" not in self.keep_classes: data[key][data[key] == 2] = 1 if "portal vein" not in self.keep_classes: data[key][data[key] == 3] = 1 if "abdominal aorta" not in self.keep_classes: data[key][data[key] >= 4] = 0 return data