| 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) |
| |
| ct_narray = np.clip(ct_narray, lower_bound, upper_bound) |
| ct_narray = (ct_narray - mean) / max(std, 1e-8) |
| return ct_narray |
| |
| @st.cache_data |
| def process_ct_gt(case_path, spatial_size=(32,256,256)): |
| if case_path is None: |
| return None |
| print('Data preprocessing...') |
| |
| 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 = {} |
| |
| 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:] |
|
|
| |
| 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 |
|
|
