Segmentation of retinal vessels by fusing contour information and conditional generative adversarial.
10.7507/1001-5515.202005019
- Author:
Liming LIANG
1
;
Zhimin LAN
1
;
Xiaoqi SHENG
1
;
Zhaoben XIE
2
;
Wanrong LIU
3
Author Information
1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P.R.China.
2. Department of Information Engineering, Gannan Medical University, Ganzhou, Jiangxi 341000, P.R.China.
3. Department of Ophthalmology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi 341000, P.R.China.
- Publication Type:Journal Article
- Keywords:
conditional generative adversarial nets;
contour loss function;
depth-wise separable convolutions;
retinal vessel segmentation;
squeeze-and-exception blocks
- MeSH:
Algorithms;
Fundus Oculi;
Optic Disk;
ROC Curve;
Retinal Vessels/diagnostic imaging*
- From:
Journal of Biomedical Engineering
2021;38(2):276-285
- CountryChina
- Language:Chinese
-
Abstract:
The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break, and the central macula and the optic disc boundary are likely to be mistakenly segmented. To solve the above problems, a novel retinal vessels segmentation algorithm is proposed in this paper. The algorithm merged together vessels contour information and conditional generative adversarial nets. Firstly, non-uniform light removal and principal component analysis were used to process the fundus images. Therefore, it enhanced the contrast between the blood vessels and the background, and obtained the single-scale gray images with rich feature information. Secondly, the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception (SE) block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion. Simultaneously, the network focused on the feature information of the learning target. Thirdly, the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network. Finally, experiments were carried out on the DRIVE and STARE datasets respectively. The value of area under the receiver operating characteristic reached 0.982 5 and 0.987 4, respectively, and the accuracy reached 0.967 7 and 0.975 6, respectively. Experimental results show that the algorithm can accurately distinguish contours and blood vessels, and reduce blood vessel rupture. The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.
