CAMU-Net:an improved model for retinal vessel segmentation based on Attention U-Net

VernacularTitle:CAMU-Net:基于Attention U-Net的视网膜血管分割改进模型
Author: Yunfei TANG ^{1
,

2} ; Zhiping DAN ; Zhengtian HONG ; Yonglin CHEN ; Peilin CHENG ; Guo CHENG ; Fangting LIU
Author Information

1. 三峡大学计算机与信息学院水电工程智能视觉监测湖北省重点实验室,湖北宜昌 443002
2. 三峡大学计算机与信息学院智慧医疗宜昌市重点实验室,湖北宜昌 430002
Keywords: retinal vessel; image segmentation; deep learning; CAMU-Net; attention mechanism
From: Chinese Journal of Medical Physics 2024;41(8):960-968
CountryChina
Language:Chinese
Abstract: An improved U-Net model(channel attention module U-Net,CAMU-Net)is proposed to achieve precise segmentation of retinal vessels.CAMU-Net model enhances its understanding of regional features by employing residual enhancement convolution to extract important information from the regions,improves the global feature acquisition capability by introducing feature refinement module to promote feature extraction,realizes precise segmentation by adding channel attention module to capture image features accurately,and enhances its capability to perceive target boundaries and details through a multi-scale feature fusion structure.The ablation study on the DRIVE dataset validates the role of each module in retinal vessel segmentation.The comparison with other mainstream network models on DRIVE and STARE datasets verify that CAMU-Net model is superior to other models.