Research on automatic delineation of nasopharyngeal carcinoma target area based on generative adversarial network
10.3760/cma.j.cn113030-20211222-00430
- VernacularTitle:基于生成对抗网络的鼻咽癌靶区自动勾画研究
- Author:
Fei WANG
1
;
Caijun REN
;
Jieping ZHOU
;
Zhenchao TAO
;
Huanhuan CHEN
;
Liting QIAN
Author Information
1. 中国科学技术大学生命科学与医学部附属第一医院，合肥 230000
- Keywords:
Deep learning;
Nasopharyngeal carcinoma;
Target area;
Automatic delineation
- From:
Chinese Journal of Radiation Oncology
2022;31(12):1127-1132
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To propose a deep learning network model 2D-PE-GAN to automatically delineate the target area of nasopharyngeal carcinoma and improve the efficiency of target area delineation.Methods:The model adopted the architecture of generative adversarial networks which used a UNet similar structure as the generator, and 2D-PE-block was added after each layer of convolution operation of the generator to improve the accuracy of delineation. The experimental data included CT images from 130 cases of nasopharyngeal carcinoma. The images were preprocessed before model training. In addition, three models of UNet, GAN, and GAN with an attention mechanism were compared, and Dice similarity coefficient, Hausdorff distance, accuracy, Matthews correlation coefficient, Jaccard distance were employed to evaluate network performance.Results:Compared with UNet, GAN and GAN with the attention mechanism, the average Dice similarity coefficient of 2D-PE-GAN network segmentation of CTV was increased by 26%, 4% and 2%. The average Dice similarity coefficient of GTV segmentation was increased by 21%, 4%, 2%, respectively. Compared with the GAN network with the attention mechanism, the parameters and time of 2D-PE-GAN were reduced by 0.16% and 18%, respectively.Conclusions:Compared with the above three networks, 2D-PE-GAN network can increase the segmentation accuracy of nasopharyngeal carcinoma target area delineation. At the same time, compared with the attention mechanism with similar reasons, 2D-PE-GAN network can reduce the occupation of computing resources when the segmentation accuracy is not much different.
