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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
@st.cache_data
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
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