writing pathology patches process functions

Author: yifanfeng97

HyperG/utils/data/__init__.py

@@ -1,2 +1,2 @@
-from .mri.read import read_mri
+from .mri.io import read_mri_series, save_mri_series
 from .data_helper import split_id

HyperG/utils/data/mri/io.py

@@ -0,0 +1,26 @@
+import os.path as osp
+
+import SimpleITK as sitk
+import matplotlib.pyplot as plt
+import torch
+
+
+def read_mri_series(mri_path):
+    img_itk = sitk.ReadImage(mri_path)
+    img_np = sitk.GetArrayFromImage(img_itk)
+    # SimpleITK read image as (z, y, x), need to be transposed to (x, y, z)
+    img_np = img_np.transpose((2, 1, 0)).astype('float')
+
+    return torch.tensor(img_np).float()
+
+
+def save_mri_series(mris: torch.Tensor, save_dir, name_prefix):
+    # mris should be image series with dimension (x, y, z)
+    assert len(mris.shape) == 3
+    # (x, y, z) -> (z, x, y)
+    mris = mris.permute(2, 0, 1)
+
+    # save each mri into a specific direction
+    for _idx in range(mris.size(0)):
+        _mri = mris[_idx].squeeze().numpy()
+        plt.imsave(osp.join(save_dir, f'{name_prefix}_{_idx}.jpg'), _mri, cmap=plt.cm.bone)

HyperG/utils/data/mri/read.py

@@ -1,2 +1,9 @@
-def sample_patch():
-    pass
+import os.path as osp
+
+import openslide
+
+
+def sample_patches(slide_dir):
+    slide = openslide.open_slide(slide_dir)
+    slide_name = osp.basename(slide_dir)
+    slide_name = slide_name[:slide_name.rfind('.')]

HyperG/utils/data/pathology/patch_funs.py

@@ -8,7 +8,7 @@
 
 from HyperG.hyedge import gather_patch_ft, neighbor_grid, neighbor_distance
 from HyperG.hygraph import hyedge_concat
-from HyperG.utils.data import split_id, read_mri
+from HyperG.utils.data import split_id, read_mri_series
 
 
 def normalize(x):
@@ -86,10 +86,10 @@ def preprocess(data_list, patch_size, k_nearest):
 
 def process_mri_seg(data, patch_size):
     # M x N x C -> C x M x N -> 1 x C x M x N
-    img = read_mri(data['img']).permute(2, 0, 1).unsqueeze(0)
+    img = read_mri_series(data['img']).permute(2, 0, 1).unsqueeze(0)
     row_num, col_num = img.size(2), img.size(3)
     # M x N x 1 -> M x N
-    lbl = read_mri(data['seg']).squeeze()
+    lbl = read_mri_series(data['seg']).squeeze()
 
     # 1 x C x M x N -> 1 x Ckk x M x N -> M x N x Ckk
     img_patched = gather_patch_ft(img, patch_size).permute(2, 3, 1, 0).squeeze()

examples/segment/heart_mri/__init__.py …mples/segmentation/heart_mri/__init__.pyexamples/segment/heart_mri/__init__.py renamed to examples/segmentation/heart_mri/__init__.py examples/segment/heart_mri/__init__.py renamed to examples/segmentation/heart_mri/__init__.py

@@ -1,14 +1,14 @@
 import pytest
 
-from HyperG.utils.data import read_mri
+from HyperG.utils.data import read_mri_series
 
 
 @pytest.mark.skip(reason='unpleasure')
 def test_read_mri():
     for _img_name in ['0001', '0002', '0003', '0004', '0005']:
         img_dir = f'/repository/HyperG_example/example_data/heart_mri/processed/{_img_name}.mha'
         save_dir = f'/repository/HyperG_example/tmp/heart_mri/{_img_name}.jpg'
-        img = read_mri(img_dir)
+        img = read_mri_series(img_dir)
 
         print(img.shape)
         import matplotlib.pyplot as plt