File size: 10,344 Bytes
e87a462 789c75e 45e75b1 e87a462 e9256f0 e87a462 45e75b1 e87a462 45e75b1 e87a462 45e75b1 e87a462 45e75b1 e87a462 45e75b1 e87a462 e9256f0 789c75e e9256f0 789c75e e9256f0 789c75e e9256f0 e87a462 2b369df e87a462 e736992 e87a462 45e75b1 0486f3c e87a462 45e75b1 e87a462 2b369df e87a462 e9256f0 2b369df e87a462 e9256f0 45e75b1 e87a462 e9256f0 e87a462 45e75b1 789c75e 45e75b1 789c75e 45e75b1 e87a462 e9256f0 45e75b1 b322ac7 45e75b1 93d3cef 45e75b1 b322ac7 45e75b1 f428da1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
import numpy as np
import gradio as gr
import cv2
from models.HybridGNet2IGSC import Hybrid
from utils.utils import scipy_to_torch_sparse, genMatrixesLungsHeart
import scipy.sparse as sp
import torch
import pandas as pd
from zipfile import ZipFile
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hybrid = None
def getDenseMask(landmarks, h, w):
RL = landmarks[0:44]
LL = landmarks[44:94]
H = landmarks[94:]
img = np.zeros([h, w], dtype = 'uint8')
RL = RL.reshape(-1, 1, 2).astype('int')
LL = LL.reshape(-1, 1, 2).astype('int')
H = H.reshape(-1, 1, 2).astype('int')
img = cv2.drawContours(img, [RL], -1, 1, -1)
img = cv2.drawContours(img, [LL], -1, 1, -1)
img = cv2.drawContours(img, [H], -1, 2, -1)
return img
def getMasks(landmarks, h, w):
RL = landmarks[0:44]
LL = landmarks[44:94]
H = landmarks[94:]
RL = RL.reshape(-1, 1, 2).astype('int')
LL = LL.reshape(-1, 1, 2).astype('int')
H = H.reshape(-1, 1, 2).astype('int')
RL_mask = np.zeros([h, w], dtype = 'uint8')
LL_mask = np.zeros([h, w], dtype = 'uint8')
H_mask = np.zeros([h, w], dtype = 'uint8')
RL_mask = cv2.drawContours(RL_mask, [RL], -1, 255, -1)
LL_mask = cv2.drawContours(LL_mask, [LL], -1, 255, -1)
H_mask = cv2.drawContours(H_mask, [H], -1, 255, -1)
return RL_mask, LL_mask, H_mask
def drawOnTop(img, landmarks, original_shape):
h, w = original_shape
output = getDenseMask(landmarks, h, w)
image = np.zeros([h, w, 3])
image[:,:,0] = img + 0.3 * (output == 1).astype('float') - 0.1 * (output == 2).astype('float')
image[:,:,1] = img + 0.3 * (output == 2).astype('float') - 0.1 * (output == 1).astype('float')
image[:,:,2] = img - 0.1 * (output == 1).astype('float') - 0.2 * (output == 2).astype('float')
image = np.clip(image, 0, 1)
RL, LL, H = landmarks[0:44], landmarks[44:94], landmarks[94:]
# Draw the landmarks as dots
for l in RL:
image = cv2.circle(image, (int(l[0]), int(l[1])), 5, (1, 0, 1), -1)
for l in LL:
image = cv2.circle(image, (int(l[0]), int(l[1])), 5, (1, 0, 1), -1)
for l in H:
image = cv2.circle(image, (int(l[0]), int(l[1])), 5, (1, 1, 0), -1)
return image
def loadModel(device):
A, AD, D, U = genMatrixesLungsHeart()
N1 = A.shape[0]
N2 = AD.shape[0]
A = sp.csc_matrix(A).tocoo()
AD = sp.csc_matrix(AD).tocoo()
D = sp.csc_matrix(D).tocoo()
U = sp.csc_matrix(U).tocoo()
D_ = [D.copy()]
U_ = [U.copy()]
config = {}
config['n_nodes'] = [N1, N1, N1, N2, N2, N2]
A_ = [A.copy(), A.copy(), A.copy(), AD.copy(), AD.copy(), AD.copy()]
A_t, D_t, U_t = ([scipy_to_torch_sparse(x).to(device) for x in X] for X in (A_, D_, U_))
config['latents'] = 64
config['inputsize'] = 1024
f = 32
config['filters'] = [2, f, f, f, f//2, f//2, f//2]
config['skip_features'] = f
hybrid = Hybrid(config.copy(), D_t, U_t, A_t).to(device)
hybrid.load_state_dict(torch.load("weights/weights.pt", map_location=torch.device(device)))
hybrid.eval()
return hybrid
def pad_to_square(img):
h, w = img.shape[:2]
if h > w:
padw = (h - w)
auxw = padw % 2
img = np.pad(img, ((0, 0), (padw//2, padw//2 + auxw)), 'constant')
padh = 0
auxh = 0
else:
padh = (w - h)
auxh = padh % 2
img = np.pad(img, ((padh//2, padh//2 + auxh), (0, 0)), 'constant')
padw = 0
auxw = 0
return img, (padh, padw, auxh, auxw)
def preprocess(input_img):
img, padding = pad_to_square(input_img)
h, w = img.shape[:2]
if h != 1024 or w != 1024:
img = cv2.resize(img, (1024, 1024), interpolation = cv2.INTER_CUBIC)
return img, (h, w, padding)
def removePreprocess(output, info):
h, w, padding = info
if h != 1024 or w != 1024:
output = output * h
else:
output = output * 1024
padh, padw, auxh, auxw = padding
output[:, 0] = output[:, 0] - padw//2
output[:, 1] = output[:, 1] - padh//2
return output
def zip_files(files):
with ZipFile("complete_results.zip", "w") as zipObj:
for idx, file in enumerate(files):
zipObj.write(file, arcname=file.split("/")[-1])
return "complete_results.zip"
def segment(input_img):
global hybrid, device
if hybrid is None:
hybrid = loadModel(device)
input_img = cv2.imread(input_img, 0) / 255.0
original_shape = input_img.shape[:2]
img, (h, w, padding) = preprocess(input_img)
data = torch.from_numpy(img).unsqueeze(0).unsqueeze(0).to(device).float()
with torch.no_grad():
output = hybrid(data)[0].cpu().numpy().reshape(-1, 2)
output = removePreprocess(output, (h, w, padding))
output = output.astype('int')
outseg = drawOnTop(input_img, output, original_shape)
seg_to_save = (outseg.copy() * 255).astype('uint8')
cv2.imwrite("tmp/overlap_segmentation.png" , cv2.cvtColor(seg_to_save, cv2.COLOR_RGB2BGR))
RL = output[0:44]
LL = output[44:94]
H = output[94:]
np.savetxt("tmp/RL_landmarks.txt", RL, delimiter=" ", fmt="%d")
np.savetxt("tmp/LL_landmarks.txt", LL, delimiter=" ", fmt="%d")
np.savetxt("tmp/H_landmarks.txt", H, delimiter=" ", fmt="%d")
RL_mask, LL_mask, H_mask = getMasks(output, original_shape[0], original_shape[1])
cv2.imwrite("tmp/RL_mask.png", RL_mask)
cv2.imwrite("tmp/LL_mask.png", LL_mask)
cv2.imwrite("tmp/H_mask.png", H_mask)
zip = zip_files(["tmp/RL_landmarks.txt", "tmp/LL_landmarks.txt", "tmp/H_landmarks.txt", "tmp/RL_mask.png", "tmp/LL_mask.png", "tmp/H_mask.png", "tmp/overlap_segmentation.png"])
return outseg, ["tmp/RL_landmarks.txt", "tmp/LL_landmarks.txt", "tmp/H_landmarks.txt", "tmp/RL_mask.png", "tmp/LL_mask.png", "tmp/H_mask.png", "tmp/overlap_segmentation.png", zip]
if __name__ == "__main__":
with gr.Blocks() as demo:
gr.Markdown("""
# Chest X-ray HybridGNet Segmentation.
Demo of the HybridGNet model introduced in "Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis."
Instructions:
1. Upload a chest X-ray image (PA or AP) in PNG or JPEG format.
2. Click on "Segment Image".
Note: Pre-processing is not needed, it will be done automatically and removed after the segmentation.
Please check citations below.
""")
with gr.Tab("Segment Image"):
with gr.Row():
with gr.Column():
image_input = gr.Image(type="filepath", height=750)
with gr.Row():
clear_button = gr.Button("Clear")
image_button = gr.Button("Segment Image")
gr.Examples(inputs=image_input, examples=['utils/example1.jpg','utils/example2.jpg','utils/example3.png','utils/example4.jpg'])
with gr.Column():
image_output = gr.Image(type="filepath", height=750)
results = gr.File()
gr.Markdown("""
If you use this code, please cite:
```
@article{gaggion2022TMI,
doi = {10.1109/tmi.2022.3224660},
url = {https://doi.org/10.1109%2Ftmi.2022.3224660},
year = 2022,
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
author = {Nicolas Gaggion and Lucas Mansilla and Candelaria Mosquera and Diego H. Milone and Enzo Ferrante},
title = {Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis},
journal = {{IEEE} Transactions on Medical Imaging}
}
```
This model was trained following the procedure explained on:
```
@misc{gaggion2022ISBI,
title={Multi-center anatomical segmentation with heterogeneous labels via landmark-based models},
author={Nicolás Gaggion and Maria Vakalopoulou and Diego H. Milone and Enzo Ferrante},
year={2022},
eprint={2211.07395},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
Example images extracted from Wikipedia, released under:
1. CC0 Universial Public Domain. Source: https://commons.wikimedia.org/wiki/File:Normal_posteroanterior_(PA)_chest_radiograph_(X-ray).jpg
2. Creative Commons Attribution-Share Alike 4.0 International. Source: https://commons.wikimedia.org/wiki/File:Chest_X-ray.jpg
3. Creative Commons Attribution 3.0 Unported. Source https://commons.wikimedia.org/wiki/File:Implantable_cardioverter_defibrillator_chest_X-ray.jpg
4. Creative Commons Attribution-Share Alike 3.0 Unported. Source: https://commons.wikimedia.org/wiki/File:Medical_X-Ray_imaging_PRD06_nevit.jpg
Author: Nicolás Gaggion
Website: [ngaggion.github.io](https://ngaggion.github.io/)
""")
clear_button.click(lambda: None, None, image_input, queue=False)
clear_button.click(lambda: None, None, image_output, queue=False)
image_button.click(segment, inputs=image_input, outputs=[image_output, results], queue=False)
demo.launch()
|