关键词: 视网膜血管分割, 空洞残差U型网络, 空洞卷积, U型网络, 残差网络

Abstract:

Glaucoma is an irreversible blinding ophthalmic disease, which should be found and treated promptly. However, the process of artificial diagnosis is time-consuming and laborious. Furthermore, it is prone to missed diagnosis and misdiagnosis due to the limitation of basic medical resources. Therefore, auxiliary diagnosis of eye diseases using deep learning technology has become more significant. How to segment the retinal blood vessels more accurately and effectively become a hot research problem for auxiliary diagnosis of eye diseases. To this end, a novel network structure based on U-Net named Atrous Residual U-Net（AR-Unet） is proposed. In AR-Unet, the Residual Network（ResNet） is firstly introduced into U-Net for the sake of avoiding the vanishing gradient and the loss of image structural information. Then, the Atrous Convolution is integrated into U-Net for expanding the receptive field and improving the correlation between objects, which makes the segmentation of blood vessels more accurate. At last, extensive experiments are conducted on three public available color fundus images databases including DRIVE, STARE and CHASE. The experimental results demonstrate that the performance of the proposed AR-Unet method is better than that of well-known methods under different evaluation criteria.

Key words: retinal vessel segmentation, Atrous Residual U-Net（AR-Unet）, atrous convolution, U-Net, Residual Network（ResNet）