| | 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 |
| |
|
