基于Retina-GAN的视网膜图像血管分割

Vessel Segmentation in Retinal Image Based on Retina-GAN

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(generative 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.