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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
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