Mr. NianZu Lv Profile

NianZu Lv

at Xinjiang Institute of Technology

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Proceedings Article | 29 December 2022 Open Access

An attention directed generative adversarial network for retinal vessel segmentation

Zhenxiang He, Nianzu Lv, Wei Sun

Proceedings Volume 12506, 125063B (2022) https://doi.org/10.1117/12.2661840

KEYWORDS: Image segmentation, Image enhancement, Blood vessels, Image processing algorithms and systems, Selenium, Convolution, Image processing, Gold, Binary data, Machine learning

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Observing the structure of retinal blood vessels can help doctors diagnose the disease of patients, so the accurate segmentation of retinal blood vessels has important research significance. However, there are many problems in retinal vessel segmentation, such as complex and small vascular structure and low image contrast, which lead to low segmentation accuracy. To solve the above problems, this paper proposes an attention-directed adversarial network for retinal vascular segmentation. The purpose is to guide the network to learn useful information for vascular segmentation and ignore useless redundant information. The attention directed generative adversarial networks consist of generator and discriminator. The generator uses U-Net architecture and combines the high-low feature attention modules. The high-low level feature attention modules act on the high-level and low-level feature maps so that the model can strengthen the high-level and low-level spatial features respectively, eliminate redundant information, and guide the model to pay more attention to the vascular foreground information. The batch normalization layer in the generator is also removed to avoid the impact of unstable batch statistics on the segmentation results when the generator is trained in small batches. The discriminator consists of a stack of residual modules, which together with the generator form a conditional generation adversarial network. The experimental results show that the Se, Sp, Acc and AUC of this paper’s method tested on the DRIVE dataset are 82.88%, 97.45%, 95.59%, and 97.86%, respectively, and all the indexes are better than the current mainstream retinal vessel segmentation algorithms.

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