# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from skimage import transform import pydicom from io import BytesIO from PIL import Image import nibabel as nib import SimpleITK as sitk from skimage import measure """ This script contains utility functions for reading and processing different imaging modalities. """ CT_WINDOWS = {'abdomen': [-150, 250], 'lung': [-1000, 1000], 'pelvis': [-55, 200], 'liver': [-25, 230], 'colon': [-68, 187], 'pancreas': [-100, 200]} def process_intensity_image(image_data, is_CT, site=None): # process intensity-based image. If CT, apply site specific windowing # image_data: 2D numpy array of shape (H, W) # return: 3-channel numpy array of shape (H, W, 3) as model input if is_CT: # process image with windowing if site and site in CT_WINDOWS: window = CT_WINDOWS[site] else: raise ValueError(f'Please choose CT site from {CT_WINDOWS.keys()}') lower_bound, upper_bound = window else: # process image with intensity range 0.5-99.5 percentile lower_bound, upper_bound = np.percentile( image_data[image_data > 0], 0.5 ), np.percentile(image_data[image_data > 0], 99.5) image_data_pre = np.clip(image_data, lower_bound, upper_bound) image_data_pre = ( (image_data_pre - image_data_pre.min()) / (image_data_pre.max() - image_data_pre.min()) * 255.0 ) # pad to square with equal padding on both sides shape = image_data_pre.shape if shape[0] > shape[1]: pad = (shape[0]-shape[1])//2 pad_width = ((0,0), (pad, pad)) elif shape[0] < shape[1]: pad = (shape[1]-shape[0])//2 pad_width = ((pad, pad), (0,0)) else: pad_width = None if pad_width is not None: image_data_pre = np.pad(image_data_pre, pad_width, 'constant', constant_values=0) # resize image to 1024x1024 image_size = 1024 resize_image = transform.resize(image_data_pre, (image_size, image_size), order=3, mode='constant', preserve_range=True, anti_aliasing=True) # convert to 3-channel image resize_image = np.stack([resize_image]*3, axis=-1) return resize_image.astype(np.uint8) def read_dicom(image_path, is_CT, site=None): # read dicom file and return pixel data # dicom_file: str, path to dicom file # is_CT: bool, whether image is CT or not # site: str, one of CT_WINDOWS.keys() # return: 2D numpy array of shape (H, W) ds = pydicom.dcmread(image_path) image_array = ds.pixel_array * ds.RescaleSlope + ds.RescaleIntercept image_array = process_intensity_image(image_array, is_CT, site) return image_array def read_nifti(image_path, is_CT, slice_idx, site=None, HW_index=(0, 1), channel_idx=None): # read nifti file and return pixel data # image_path: str, path to nifti file # is_CT: bool, whether image is CT or not # slice_idx: int, slice index to read # site: str, one of CT_WINDOWS.keys() # HW_index: tuple, index of height and width in the image shape # return: 2D numpy array of shape (H, W) nii = nib.load(image_path) image_array = nii.get_fdata() if HW_index != (0, 1): image_array = np.moveaxis(image_array, HW_index, (0, 1)) # get slice if channel_idx is None: image_array = image_array[:, :, slice_idx] else: image_array = image_array[:, :, slice_idx, channel_idx] image_array = process_intensity_image(image_array, is_CT, site) return image_array def read_rgb(image_path): # read RGB image and return resized pixel data # image_path: str, path to RGB image # return: BytesIO buffer # read image into numpy array image = Image.open(image_path) image = np.array(image) if len(image.shape) == 2: image = np.stack([image]*3, axis=-1) elif image.shape[2] == 4: image = image[:,:,:3] # pad to square with equal padding on both sides shape = image.shape if shape[0] > shape[1]: pad = (shape[0]-shape[1])//2 pad_width = ((0,0), (pad, pad), (0,0)) elif shape[0] < shape[1]: pad = (shape[1]-shape[0])//2 pad_width = ((pad, pad), (0,0), (0,0)) else: pad_width = None if pad_width is not None: image = np.pad(image, pad_width, 'constant', constant_values=0) # resize image to 1024x1024 for each channel image_size = 1024 resize_image = np.zeros((image_size, image_size, 3), dtype=np.uint8) for i in range(3): resize_image[:,:,i] = transform.resize(image[:,:,i], (image_size, image_size), order=3, mode='constant', preserve_range=True, anti_aliasing=True) return resize_image def get_instances(mask): # get intances from binary mask seg = sitk.GetImageFromArray(mask) filled = sitk.BinaryFillhole(seg) d = sitk.SignedMaurerDistanceMap(filled, insideIsPositive=False, squaredDistance=False, useImageSpacing=False) ws = sitk.MorphologicalWatershed( d, markWatershedLine=False, level=1) ws = sitk.Mask( ws, sitk.Cast(seg, ws.GetPixelID())) ins_mask = sitk.GetArrayFromImage(ws) # filter out instances with small area outliers props = measure.regionprops_table(ins_mask, properties=('label', 'area')) mean_area = np.mean(props['area']) std_area = np.std(props['area']) threshold = mean_area - 2*std_area - 1 ins_mask_filtered = ins_mask.copy() for i, area in zip(props['label'], props['area']): if area < threshold: ins_mask_filtered[ins_mask == i] = 0 return ins_mask_filtered