Journal
14TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND EDUCATION (ICCSE 2019)
Volume -, Issue -, Pages 642-646Publisher
IEEE
DOI: 10.1109/iccse.2019.8845397
Keywords
Generative Adversarial Networks; attention gate; densely-connected convolutional network
Categories
Funding
- Hubei Provincial Natural Science Foundation of China [2012FFB00701]
- Doctoral Scientific Research Foundation of Hubei University of Technology [BSQD12120, BSQD2015026]
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The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. However, existing methods have various problems in the segmentation of the retinal vessels, such as insufficient segmentation of retinal vessels, weak anti-noise interference ability. Aiming to the shortcomings of existed methods, this paper proposes an improved model based on the Generative Adversarial Networks with U-Net, which contains densely-connected convolutional network and a novel attention gate (AG) model in the generator, referred as U-GAN, to automatically segment the retinal blood vessels. The method can strengthen feature propagation, substantially reduce the number of parameters, and automatically learn to focus on target structures without additional supervision. By verifying the method on the DRIVE datasets, the segmentation accuracy rate is 96.15%, higher than that of U-Net and R2U-Net.
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