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