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| import numpy as np | |
| import monai.transforms as transforms | |
| import streamlit as st | |
| import tempfile | |
| class MinMaxNormalization(transforms.Transform): | |
| def __call__(self, data): | |
| d = dict(data) | |
| k = "image" | |
| d[k] = d[k] - d[k].min() | |
| d[k] = d[k] / np.clip(d[k].max(), a_min=1e-8, a_max=None) | |
| return d | |
| class DimTranspose(transforms.Transform): | |
| def __init__(self, keys): | |
| self.keys = keys | |
| def __call__(self, data): | |
| d = dict(data) | |
| for key in self.keys: | |
| d[key] = np.swapaxes(d[key], -1, -3) | |
| return d | |
| class ForegroundNormalization(transforms.Transform): | |
| def __init__(self, keys): | |
| self.keys = keys | |
| def __call__(self, data): | |
| d = dict(data) | |
| for key in self.keys: | |
| d[key] = self.normalize(d[key]) | |
| return d | |
| def normalize(self, ct_narray): | |
| ct_voxel_ndarray = ct_narray.copy() | |
| ct_voxel_ndarray = ct_voxel_ndarray.flatten() | |
| thred = np.mean(ct_voxel_ndarray) | |
| voxel_filtered = ct_voxel_ndarray[(ct_voxel_ndarray > thred)] | |
| upper_bound = np.percentile(voxel_filtered, 99.95) | |
| lower_bound = np.percentile(voxel_filtered, 00.05) | |
| mean = np.mean(voxel_filtered) | |
| std = np.std(voxel_filtered) | |
| ### transform ### | |
| ct_narray = np.clip(ct_narray, lower_bound, upper_bound) | |
| ct_narray = (ct_narray - mean) / max(std, 1e-8) | |
| return ct_narray | |
| def process_ct_gt(case_path, spatial_size=(32,256,256)): | |
| if case_path is None: | |
| return None | |
| print('Data preprocessing...') | |
| # transform | |
| img_loader = transforms.LoadImage(dtype=np.float32) | |
| transform = transforms.Compose( | |
| [ | |
| transforms.Orientationd(keys=["image"], axcodes="RAS"), | |
| ForegroundNormalization(keys=["image"]), | |
| DimTranspose(keys=["image"]), | |
| MinMaxNormalization(), | |
| transforms.SpatialPadd(keys=["image"], spatial_size=spatial_size, mode='constant'), | |
| transforms.CropForegroundd(keys=["image"], source_key="image"), | |
| transforms.ToTensord(keys=["image"]), | |
| ] | |
| ) | |
| zoom_out_transform = transforms.Resized(keys=["image"], spatial_size=spatial_size, mode='nearest-exact') | |
| z_transform = transforms.Resized(keys=["image"], spatial_size=(325,325,325), mode='nearest-exact') | |
| ### | |
| item = {} | |
| # generate ct_voxel_ndarray | |
| if type(case_path) is str: | |
| ct_voxel_ndarray, meta_tensor_dict = img_loader(case_path) | |
| else: | |
| bytes_data = case_path.read() | |
| with tempfile.NamedTemporaryFile(suffix='.nii.gz') as tmp: | |
| tmp.write(bytes_data) | |
| tmp.seek(0) | |
| ct_voxel_ndarray, meta_tensor_dict = img_loader(tmp.name) | |
| ct_voxel_ndarray = np.array(ct_voxel_ndarray).squeeze() | |
| ct_voxel_ndarray = np.expand_dims(ct_voxel_ndarray, axis=0) | |
| item['image'] = ct_voxel_ndarray | |
| ori_shape = np.swapaxes(ct_voxel_ndarray, -1, -3).shape[1:] | |
| # transform | |
| item = transform(item) | |
| item_zoom_out = zoom_out_transform(item) | |
| item['zoom_out_image'] = item_zoom_out['image'] | |
| item['ori_shape'] = ori_shape | |
| item_z = z_transform(item) | |
| item['z_image'] = item_z['image'] | |
| item['meta'] = meta_tensor_dict | |
| return item | |