摘要
Abstract
Retinal vessel segmentation is crucial for the diagnosis and treatment of fundus disorders,and early detection and intervention can alleviate visual impairment and even prevent blindness.However,Since insufficient feature extraction can lead to the loss of vascular information,accurately segmenting blood vessels remains a significant challente.Based on this,this paper proposes a retinal vessel segmentation method based on multi-scale fusion attention network.First,we de-sign a multi-branch residual module,which enables better aggregation of low-level features with high-level features and im-proves the feature extraction capability of the network.Next,a parallel dilation convolution module is designed and embed-ded into the decoding and encoding ends to enhance the modeling sensory field while acquiring richer contextual information.Finally,a multi-scale fusion attention module is designed to enhance feature extraction.The model in this paper is experi-mented on three publicly available fundus image datasets,DRIVE,STARE,and CHASEDB1,compared to the baseline net-work,the experimental results show that the model's segmentation accuracy has improved across the board,its segmenta-tion sensitivity has increased by 0.28％,1.29％and 1.3％,respectively.关键词
视网膜血管分割/眼底图像/多尺度融合注意力Key words
retinal vessel segmentation/fundus image/multi-scale fusion attention分类
信息技术与安全科学
