Automatic Post-operative Cervical Cancer Target Area and Organ at Risk Outlining Based on Fusion Convolutional Neural Network.
10.3969/j.issn.1671-7104.2022.02.003
- Author:
Jin ZHOU
1
;
Wei YANG
2
;
Shanshan GU
2
;
Hong QUAN
1
;
Jie LIU
3
;
Zhongjian JU
2
Author Information
1. School of Physics Science and Technology, Wuhan University, Wuhan,
2. Radiotherapy Department, First Medical Center, General Hospital of Chinese People's Liberation Army, Beijing,
3. Beijing Eastraycloud Technology Inc., Beijing,
- Publication Type:Journal Article
- Keywords:
automatic outline;
cervical cancer;
deep learning;
fusion neural network;
radiotherapy
- MeSH:
Female;
Humans;
Image Processing, Computer-Assisted;
Neural Networks, Computer;
Organs at Risk;
Pelvis;
Tomography, X-Ray Computed;
Uterine Cervical Neoplasms/surgery*
- From:
Chinese Journal of Medical Instrumentation
2022;46(2):132-136
- CountryChina
- Language:Chinese
-
Abstract:
CT image based organ segmentation is essential for radiotherapy treatment planning, and it is laborious and time consuming to outline the endangered organs and target areas before making radiation treatment plans. This study proposes a fully automated segmentation method based on fusion convolutional neural network to improve the efficiency of physicians in outlining the endangered organs and target areas. The CT images of 170 postoperative cervical cancer stage IB and IIA patients were selected for network training and automatic outlining of bladder, rectum, femoral head and CTV, and the neural network was used to localize easily distinguishable vessels around the target area to achieve more accurate outlining of CTV.
