Multi-channel conditional generative adversarial networks retinal vessel segmentation algorithm
10.3760/cma.j.issn.2095-0160.2019.08.006
- VernacularTitle:多通道条件生成对抗网络视网膜血管分割算法
- Author:
Cheng WAN
1
;
Yikuang WANG
;
Peiyuan XU
;
Jianxin SHEN
;
Zhiqiang CHEN
Author Information
1. 南京航空航天大学电子信息工程学院 211106
- Keywords:
Retinal fundus images;
Vessel segmentation;
Medical image processing;
Deep learning;
Conditional generative adversarial networks
- From:
Chinese Journal of Experimental Ophthalmology
2019;37(8):619-623
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a model for accurately segmenting blood vessels in medical fundus images. Methods The algorithm of deep learning was used for the task of automatic segmentation of blood vessels in retinal fundus images in this paper. An improved vascular segmentation algorithm was proposed. For the different types of blood vessels in the fundus image, a multi-scale network structure was designed to extract features of both main blood vessels and vessel branches at the same time. Results The segmentation model proposed could achieve good results on all kinds of blood vessels even if they have low contrast and few obvious characteristics. The automatic vessel segmentation of retinal fundus images was implemented, and the performance of the model was evaluated through multiple evaluation indexes which are widely used in the field of medical image segmentation in the test stage. A specificity of 0. 9829,an F1 score of 0. 7944,a G-mean of 0. 8748,an Matthews correlation coefficient(MCC) of 0. 7764 and a specificity of 0. 9782 were obtained on the DRIVE dataset. An F1 score of 0. 7735 and an MCC of 0. 7573 were obtained on the STARE data set. Conclusions The proposed method has a great improvement over the segmentation algorithm of the same task. Furthermore,the results generated by our model can achieve comparable effect with the segmentation of human doctor.
