Automated Pre-delineation of CTV in Patients with Cervical Cancer Using Dense V-Net.
10.3969/j.issn.1671-7104.2020.05.007
- Author:
Wen GUO
1
;
Zhongjian JU
2
;
Wei YANG
2
;
Shanshan GU
2
;
Jin ZHOU
1
;
Xiaohu CONG
2
;
Jie LIU
3
;
Xiangkun DAI
2
Author Information
1. School of Physics Science and Technology, Wuhan University, Wuhan, 430072.
2. Radiotherapy Department, First Medical Center, General Hospital of Chinese People's Liberation Army, Beijing, 100853.
3. Beijing Eastraycloud Technology Inc., Beijing, 100020.
- Publication Type:Journal Article
- Keywords:
automatic delineation;
cervical cancer;
clinical target volume;
convolutional neural network;
deep learning
- MeSH:
Automation;
Female;
Humans;
Machine Learning;
Patients;
Tomography, X-Ray Computed;
Uterine Cervical Neoplasms/diagnostic imaging*
- From:
Chinese Journal of Medical Instrumentation
2020;44(5):409-414
- CountryChina
- Language:Chinese
-
Abstract:
We use a dense and fully connected convolutional network with good feature learning in small samples, to automatically pre-deline CTV of cervical cancer patients based on CT images and evaluate the effect. The CT data of stage IB and IIA postoperative cervical cancer with similar delineation scope were selected to be used to evaluate the pre-sketching accuracy from three aspects:sketching similarity, sketching offset and sketching volume difference. It has been proved that the 8 most representative parameters are superior to those with single network and reported internationally before. Dense V-Net can accurately predict CTV pre-delineation of cervical cancer patients, which can be used clinically after simple modification by doctors.
