对于一些可以从视网膜血管观测到的眼科疾病, 眼底图像起着关键的作用, 能够为专业的医科人员提供有效的参考, 然而手工标注血管费时费力, 且工作量较大, 所以实现自动智能的血管分割方法对相关人员大有裨益. 本文将Attention机制与RU-Net结构融合应用到生成对抗网络(generating adversarial network, GAN)的生成器中, 形成了一种新的结构——Retina-GAN. 同时在对眼底图像的预处理步骤上选择了自动色彩均衡 (ACE), 提高图像对比度, 使血管更加清晰. 为了验证所提出的方法, 选用DRIVE数据集, 并把Retina-GAN与其他研究比照, 测量分析了算法准确性、灵敏度和特异度. 实验数据显示Retina-GAN比其他模型具有更好的性能.
For finding the ophthalmic diseases that can be observed from retinal vessels, fundus images play a key role and provide an effective reference for professional medical personnel. However, manual vessel segmentation has a large workload, which is time-consuming and laborious. Therefore, developing an automatic and intelligent segmentation method is of great benefit to relevant personnel. In this study, the attention mechanism and RU-Net structure are integrated into the generator of generative adversarial networks (GANs), forming a new structure—Retina-GAN. At the same time, automatic color equalization (ACE) is selected in the preprocessing of fundus images to improve image contrast and make blood vessels clearer. To validate the proposed approach, we compared the Retina-GAN with some other models on DRIVE datasets. Accuracy, sensitivity, and specificity are measured for comparative analysis. The experiment shows that Retina-GAN has better performance than other models.