Migrated from GitHub

.gitattributes

@@ -57,3 +57,27 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/data/registration/post_expansion/example1.tif filter=lfs diff=lfs merge=lfs -text
+data/data/registration/post_expansion/example2.tif filter=lfs diff=lfs merge=lfs -text
+data/data/registration/pre_expansion/example1.tif filter=lfs diff=lfs merge=lfs -text
+data/data/registration/pre_expansion/example2.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-1.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-10.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-2.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-3.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-4.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-5.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-6.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-7.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-8.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example1.tif/seg-example1-9.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-1.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-10.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-2.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-3.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-4.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-5.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-6.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-7.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-8.tif filter=lfs diff=lfs merge=lfs -text
+data/data/trailmap_volumes/seg-example2.tif/seg-example2-9.tif filter=lfs diff=lfs merge=lfs -text

data/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Kamran Ahmed
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

data/data/expansion_factors.csv

@@ -0,0 +1,3 @@
+id,expansion_factor
+example1,4.172762498448865
+example2,4.234239813334196

data/data/segmentation_data.csv

@@ -0,0 +1,3 @@
+id,image_volume (um3),axon_volume (um3),axon_length (um),avg_axon_radius (um)
+seg-example1.tif,2192.190448787476,40.14157371180198,676.9529106394549,0.10804224759340286
+seg-example2.tif,2098.084266317545,97.1325822783973,1589.9739226329862,0.11063189804553986

data/process_batch.py

@@ -0,0 +1,43 @@
+import os
+import pandas as pd
+import sys
+from processing import process_volume, measure
+
+if __name__ == "__main__":
+    base_path = os.path.abspath(__file__ + "/..")
+
+    expansion_factors = pd.read_csv(base_path + "/data/expansion_factors.csv")
+    expansion_factors_dict = {}
+
+    input_batch = sys.argv[1:]
+
+    for input_folder in input_batch:
+        if not os.path.isdir(input_folder):
+            raise Exception(input_folder + " is not a directory. Inputs must be a folder of files.")
+        
+        row = expansion_factors[expansion_factors["id"]  == os.path.basename(input_folder)[4:-4]]
+        expansion_factor = row["expansion_factor"].values[0]
+
+        if not expansion_factor:
+            raise Exception(input_folder + " does not have an expansion factor.")
+        
+        expansion_factors_dict[input_folder] = expansion_factor
+    
+    assert len(input_batch) == len(expansion_factors_dict), "Collisions when creating expansion_factor dictionary (i.e. one to many relationship)."
+
+    print(f"Number of volumes: {len(expansion_factors_dict)}")
+
+    save_file = base_path + "/data/segmentation_data.csv"
+
+    with open(save_file, "w") as f:
+        f.write("id,image_volume (um3),axon_volume (um3),axon_length (um),avg_axon_radius (um)\n")
+
+    for input_folder, expansion_factor in expansion_factors_dict.items():
+        name = os.path.basename(input_folder)
+        print(f"Processing {name}")
+
+        vol = process_volume(input_folder)
+        data = measure(vol, expansion_factor)
+        
+        with open(save_file, "a") as f:
+            f.write(f"{name},{data[0]},{data[1]},{data[2]},{data[3]}\n")

data/processing/__init__.py

@@ -0,0 +1,2 @@
+from processing.measure import measure
+from processing.process import process_volume

data/processing/measure.py

@@ -0,0 +1,32 @@
+import numpy as np
+from edt import edt3d
+from skan import Skeleton, summarize
+from skimage.morphology import skeletonize_3d
+
+
+"""
+Lateral and axial lightsheet resolution (microns).
+"""
+x_res = 0.091000116097948115
+z_res = 0.52175056847846326
+
+
+def measure(vol, expansion_factor):   
+    voxel_size = x_res * x_res * z_res
+    sampling = (z_res, x_res, x_res)
+
+    normalized_voxel_size = voxel_size / (expansion_factor ** 3)
+    normalized_sampling = tuple(dim / expansion_factor for dim in sampling)
+
+    total_img_volume = vol.size * normalized_voxel_size
+    total_axon_volume = np.count_nonzero(vol) * normalized_voxel_size
+
+    skeleton = skeletonize_3d(vol)
+    branch_data = summarize(Skeleton(skeleton, spacing=normalized_sampling))
+    total_axon_length = branch_data["branch-distance"].sum()
+
+    # significantly faster than scipy.ndimage.distance_transform_edt
+    distance_transform = edt3d(vol, anisotropy=normalized_sampling, black_border=False, order="C", parallel=10)
+    avg_axon_radius = np.mean(distance_transform[skeleton.astype(bool)])
+
+    return [total_img_volume, total_axon_volume, total_axon_length, avg_axon_radius]

data/processing/process.py

@@ -0,0 +1,31 @@
+import cv2
+import numpy as np
+import os
+from skimage.morphology import remove_small_objects
+
+
+def read_folder_volume(path):
+    tiffs = [os.path.join(path, f) for f in os.listdir(path) if f[0] != '.']
+    fnames = sorted(tiffs)
+    
+    vol = []
+
+    for i, fname in enumerate(fnames):
+        img = cv2.imread(fname, cv2.COLOR_BGR2GRAY)
+        vol.append(img)
+
+    vol = np.array(vol)
+
+    return vol
+
+
+def binarize(array, threshold_value):
+    return (array > threshold_value)
+
+
+def process_volume(path):
+    vol = read_folder_volume(path)
+    threshold = binarize(vol, 0.7)
+    filtered = remove_small_objects(threshold, min_size=256, connectivity=3)
+
+    return filtered

data/register_batch.py

@@ -0,0 +1,34 @@
+import cv2
+import os
+from registration import rigid_registration
+
+
+def get_images(path):
+    images = [os.path.join(path, f) for f in os.listdir(path) if f[0] != "."]
+    return sorted(images)
+
+
+if __name__ == "__main__":
+    base_path = os.path.abspath(__file__ + "/..")
+
+    pre_folder = get_images(base_path + "/data/registration/pre_expansion")
+    post_folder = get_images(base_path + "/data/registration/post_expansion")
+
+    assert len(pre_folder) == len(post_folder), "Unequal number of images. Pre- and post-expansion directories must have a 1-to-1 matching of files."
+
+    save_file = base_path + "/data/expansion_factors.csv"
+    
+    with open(save_file, "w") as f:
+        f.write("id,expansion_factor\n")
+
+    for i in range(len(pre_folder)):
+        name = os.path.basename(pre_folder[i])
+        name, _ = os.path.splitext(name)
+        
+        pre = cv2.imread(pre_folder[i], cv2.COLOR_BGR2GRAY)
+        post = cv2.imread(post_folder[i], cv2.COLOR_BGR2GRAY)
+
+        expansion_factor = rigid_registration(pre, post)
+
+        with open(save_file, "a") as f:
+            f.write(f"{name},{expansion_factor}\n")

data/registration/__init__.py

@@ -0,0 +1 @@
+from registration.rigid_registration import rigid_registration

data/registration/rigid_registration.py

@@ -0,0 +1,56 @@
+import numpy as np
+import cv2
+
+
+def rescale_and_convert(image):
+    lower_bound, upper_bound = np.percentile(image, (0, 98))
+
+    image = np.clip(image, lower_bound, upper_bound)
+    image = (image - lower_bound) / (upper_bound - lower_bound)
+
+    return np.asarray(image * (2 ** 8 - 1), dtype=np.uint8)
+
+
+def rigid_registration(pre, post, h_flip=False):
+    if not h_flip:
+        if pre.dtype != np.uint8:
+            pre = rescale_and_convert(pre)    
+        if post.dtype != np.uint8:
+            post = rescale_and_convert(post)
+
+    sift = cv2.xfeatures2d.SIFT_create(sigma=1.6)
+    kp1, des1 = sift.detectAndCompute(post, None)
+    kp2, des2 = sift.detectAndCompute(pre, None)
+
+    bf = cv2.BFMatcher()
+    matches = bf.knnMatch(des1, des2, k=2)
+
+    good = []
+    for m, n in matches:
+        if m.distance < 0.7 * n.distance:
+            good.append(m)
+
+    MIN_MATCH_COUNT = 10
+
+    if len(good) > MIN_MATCH_COUNT:
+        print("\x1b[32mSuccess! Enough matches found: %d>%d\x1b[0m."% (len(good), MIN_MATCH_COUNT), "Horizontal flip:", h_flip)
+
+        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
+        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
+
+        M, _ = cv2.estimateAffinePartial2D(src_pts, dst_pts, method=cv2.RANSAC)
+ 
+        cos_scale = M[0, 0]
+        sin_scale = M[0, 1]
+
+        expansion_factor = 1 / ((cos_scale ** 2 + sin_scale ** 2) ** 0.5)
+
+    elif not h_flip:
+        print("\x1b[31mFailure! Not enough matches are found: %d<%d\x1b[0m."% (len(good), MIN_MATCH_COUNT), "Attempting horizontal flip...")
+        return rigid_registration(pre, post[:, ::-1], h_flip=True)
+
+    else:
+        print("\x1b[31mFailure! Not enough matches are found: %d<%d\x1b[0m."% (len(good), MIN_MATCH_COUNT), "Horizontal flip:", h_flip)
+        expansion_factor = None
+
+    return expansion_factor