A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs.
10.7507/1001-5515.201809011
- Author:
Qingnan WU
1
,
2
;
Yunlai WANG
3
;
Hong QUAN
2
;
Junjie WANG
4
;
Shanshan GU
3
;
Wei YANG
3
;
Ruigang GE
3
;
Jie LIU
5
;
Zhongjian JU
3
Author Information
1. Department of Radiation Oncology, Peking University International Hospital, Beijing 102206, P.R.China
2. School of Physics Science and Technology, Wuhan University, Wuhan 430072, P.R.China.
3. Department of Radiation Oncology, People's Liberation Army General Hospital, Beijing 100853, P.R.China.
4. Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, P.R.China.
5. Beijing Oriental Ruiyun Technology Corporation, Beijing 100020, P.R.China.
- Publication Type:Journal Article
- Keywords:
automatic segmentation;
convolutional neural networks;
deep learning;
multiple model fusion;
organ at risk delineation
- MeSH:
Algorithms;
Humans;
Image Processing, Computer-Assisted;
Imaging, Three-Dimensional;
Neural Networks, Computer;
Organs at Risk;
Pelvis;
Tomography, X-Ray Computed
- From:
Journal of Biomedical Engineering
2020;37(2):311-316
- CountryChina
- Language:Chinese
-
Abstract:
When applying deep learning to the automatic segmentation of organs at risk in medical images, we combine two network models of Dense Net and V-Net to develop a Dense V-network for automatic segmentation of three-dimensional computed tomography (CT) images, in order to solve the problems of degradation and gradient disappearance of three-dimensional convolutional neural networks optimization as training samples are insufficient. This algorithm is applied to the delineation of pelvic endangered organs and we take three representative evaluation parameters to quantitatively evaluate the segmentation effect. The clinical result showed that the Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 (average was 0.9); Jaccard distance of these were within 2.3 (average was 0.18). Except for the small intestine, the Hausdorff distance of other organs were less than 0.9 cm (average was 0.62 cm). The Dense V-Network has been proven to achieve the accurate segmentation of pelvic endangered organs.
