update scripts

scripts/DL_save_nifti.py

@@ -1,55 +1,28 @@
-#!/usr/bin/env python
-"""
-Ke Yan
-Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
-National Institutes of Health Clinical Center
-May 2018
-THIS SOFTWARE IS PROVIDED BY THE AUTHOR(S) ``AS IS'' AND ANY EXPRESS OR
-IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
-OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
-IN NO EVENT SHALL THE AUTHOR(S) BE LIABLE FOR ANY DIRECT, INDIRECT,
-INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
-NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
-THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-"""
-
 """
 A simple demo to load 2D 16-bit slices from DeepLesion and save to 3D nifti volumes.
 The nifti volumes can be viewed in software such as 3D slicer and ITK-SNAP.
 """
 
-
-import numpy as np
-import nibabel as nib
 import os
 import cv2
-import csv
+
+import numpy as np
+import pandas as pd
+import SimpleITK as sitk
 
 
-dir_in = 'Images_png'
-dir_out = 'Images_nifti'
-out_fmt = '%s_%03d-%03d.nii.gz'  # format of the nifti file name to output
-info_fn = 'DL_info.csv'  # file name of the information file
+dir_in = '../Images_png'
+dir_out = '../Images_nifti'
+info_fn = '../DL_info.csv'
 
 
 def slices2nifti(ims, fn_out, spacing):
     """save 2D slices to 3D nifti file considering the spacing"""
-    if len(ims) < 300:  # cv2.merge does not support too many channels
-        V = cv2.merge(ims)
-    else:
-        V = np.empty((ims[0].shape[0], ims[0].shape[1], len(ims)))
-        for i in range(len(ims)):
-            V[:, :, i] = ims[i]
-
-    # the transformation matrix suitable for 3D slicer and ITK-SNAP
-    T = np.array([[0, -spacing[1], 0, 0], [-spacing[0], 0, 0, 0], [0, 0, -spacing[2], 0], [0, 0, 0, 1]])
-    img = nib.Nifti1Image(V, T)
-    path_out = os.path.join(dir_out, fn_out)
-    nib.save(img, path_out)
-    print fn_out, 'saved'
+    image_itk = sitk.GetImageFromArray(np.stack(ims, axis=0))
+    image_itk.SetSpacing(spacing)
+    image_itk.SetDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0))
+    sitk.WriteImage(image_itk, os.path.join(dir_out, fn_out))
+    print(fn_out, 'saved')
 
 
 def load_slices(dir, slice_idxs):
@@ -58,43 +31,27 @@ def load_slices(dir, slice_idxs):
     assert np.all(slice_idxs[1:] - slice_idxs[:-1] == 1)
     ims = []
     for slice_idx in slice_idxs:
-        fn = '%03d.png' % slice_idx
-        path = os.path.join(dir_in, dir, fn)
-        im = cv2.imread(path, -1)  # -1 is needed for 16-bit image
-        assert im is not None, 'error reading %s' % path
-        print 'read', path
+        path = os.path.join(dir_in, dir, f'{slice_idx:03d}.png')
+        im = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # Read as 16-bit image
+        assert im is not None, f'error reading {path}'
+        print(f'read {path}')
 
-        # the 16-bit png file has a intensity bias of 32768
+        # the 16-bit png file has an intensity bias of 32768
         ims.append((im.astype(np.int32) - 32768).astype(np.int16))
     return ims
 
 
-def read_DL_info():
-    """read spacings and image indices in DeepLesion"""
-    spacings = []
-    idxs = []
-    with open(info_fn, 'rb') as csvfile:
-        reader = csv.reader(csvfile)
-        rownum = 0
-        for row in reader:
-            if rownum == 0:
-                header = row
-                rownum += 1
-            else:
-                idxs.append([int(d) for d in row[1:4]])
-                spacings.append([float(d) for d in row[12].split(',')])
-
-    idxs = np.array(idxs)
-    spacings = np.array(spacings)
-    return idxs, spacings
+if __name__ == '__main__':
 
+    # Read spacings and image indices in DeepLesion
+    dl_info = pd.read_csv(info_fn)
+    idxs = dl_info[['Patient_index', 'Study_index', 'Series_ID']].values
+    spacings = dl_info['Spacing_mm_px_'].apply(lambda x: np.array(x.split(", "), dtype=float)).values
+    spacings = np.stack(spacings)
 
-if __name__ == '__main__':
-    idxs, spacings = read_DL_info()
     if not os.path.exists(dir_out):
         os.mkdir(dir_out)
-    img_dirs = os.listdir(dir_in)
-    img_dirs.sort()
+    img_dirs = sorted(os.listdir(dir_in))
     for dir1 in img_dirs:
         # find the image info according to the folder's name
         idxs1 = np.array([int(d) for d in dir1.split('_')])
@@ -102,8 +59,7 @@ if __name__ == '__main__':
         spacings1 = spacings[i1[0]]
 
         fns = os.listdir(os.path.join(dir_in, dir1))
-        slices = [int(d[:-4]) for d in fns if d.endswith('.png')]
-        slices.sort()
+        slices = sorted([int(d[:-4]) for d in fns if d.endswith('.png')])
 
         # Each folder contains png slices from one series (volume)
         # There may be several sub-volumes in each volume depending on the key slices
@@ -118,5 +74,5 @@ if __name__ == '__main__':
         for group in groups:
             # group contains slices indices of a sub-volume
             ims = load_slices(dir1, group)
-            fn_out = out_fmt % (dir1, group[0], group[-1])
+            fn_out = f'{dir1}_{group[0]:03d}-{group[-1]:03d}.nii.gz'
             slices2nifti(ims, fn_out, spacings1)

scripts/LICENSE

@@ -0,0 +1,15 @@
+Copyright (c) Feb 2024, Benjamin Hou
+Copyright (c) May 2018, Ke Yan
+Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
+National Institutes of Health Clinical Center
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR(S) ``AS IS'' AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+IN NO EVENT SHALL THE AUTHOR(S) BE LIABLE FOR ANY DIRECT, INDIRECT,
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

scripts/apply_window.py

@@ -0,0 +1,54 @@
+"""
+Apply CT windowing parameter from DL_info.csv to Images_png
+"""
+
+import os
+import cv2
+
+import numpy as np
+import pandas as pd
+
+from glob import glob
+from tqdm import tqdm
+
+
+dir_in = '../Images_png'
+dir_out = '../Images_png_wn'
+info_fn = '../DL_info.csv'
+
+if not os.path.exists(dir_out):
+    os.mkdir(dir_out)
+
+dl_info = pd.read_csv(info_fn)
+
+def clip_and_normalize(np_image: np.ndarray,
+                       clip_min: int = -150,
+                       clip_max: int = 250
+                       ) -> np.ndarray:
+    np_image = np.clip(np_image, clip_min, clip_max)
+    np_image = (np_image - clip_min) / (clip_max - clip_min)
+    return np_image
+
+
+for idx, row in tqdm(dl_info.iterrows(), total=len(dl_info)):
+
+    folder = row['File_name'].rsplit('_', 1)[0]
+    images = sorted(glob(f'{dir_in}/{folder}/*.png'))
+
+    if not os.path.exists(f'{dir_out}/{folder}'):
+        os.mkdir(f'{dir_out}/{folder}')
+    DICOM_windows = [float(value.strip()) for value in row['DICOM_windows'].split(',')]
+
+    for im in images:
+        try:
+            image = cv2.imread(im, cv2.IMREAD_UNCHANGED)
+            image = image.astype('int32') - 32768
+            image = clip_and_normalize(image, *DICOM_windows)
+            image = (image*255).astype('uint8')
+            cv2.imwrite(f'{dir_out}/{folder}/{os.path.basename(im)}', image)
+        except AttributeError:
+            # Broken Images
+            # 001821_07_01/372.png
+            # 002161_04_02/116.png
+            print(f'Conversion failed: {im}')
+            continue

scripts/batch_download_zips.py

@@ -1,5 +1,19 @@
-# Download the 56 zip files in Images_png in batches
-import urllib
+"""
+Download the 56 zip files in Images_png in batches
+"""
+
+import os
+import urllib.request
+
+from tqdm import tqdm
+
+
+class DownloadProgressBar(tqdm):
+    def update_to(self, b=1, bsize=1, tsize=None):
+        if tsize is not None:
+            self.total = tsize
+        self.update(b * bsize - self.n)
+
 
 # URLs for the zip files
 links = [
@@ -66,10 +80,18 @@ links = [
     'https://nihcc.box.com/shared/static/kqg4peb9j53ljhrxe3l3zrj4ac6xogif.zip'
 ]
 
+# Download directory 
+dir_out = '../Images_zip'
+if not os.path.exists(dir_out):
+    os.mkdir(dir_out)
+
+# Download MD5 checksum file
 md5_link = 'https://nihcc.box.com/shared/static/q0f8gy79q2spw96hs6o4jjjfsrg17t55.txt'
-urllib.urlretrieve(md5_link, "MD5_checksums.txt")  # download the MD5 checksum file
+urllib.request.urlretrieve(md5_link, f'{dir_out}/MD5_checksums.txt')
+
+# Download DeepLesion zip files
 for idx, link in enumerate(links):
-    fn = 'Images_png_%02d.zip' % (idx+1)
-    print 'downloading', fn, '...'
-    urllib.urlretrieve(link, fn)  # download the zip file
-print "Download complete. Please check the MD5 checksums"
+    fn = f'{dir_out}/Images_png_{idx+1:02d}.zip'
+    with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=f'Downloading: {fn}') as t:
+        urllib.request.urlretrieve(link, fn, t.update_to)
+print('Download complete. Please check the MD5 checksums')