Application of residual U-Net combined with three-space attention in retinal vessel segmentation
10.3969/j.issn.1005-202X.2024.06.010
- VernacularTitle:三空间注意力的残差U-Net在视网膜血管分割应用
- Author:
Yiliu HANG
1
;
Qiong ZHANG
;
Jianlin QIU
;
Yuwei YANG
Author Information
1. 南通理工学院计算机与信息工程学院,江苏南通 226000
- Keywords:
retinal vessel;
deep learning;
multi-level residual;
three-space attention;
U-Net
- From:
Chinese Journal of Medical Physics
2024;41(6):724-733
- CountryChina
- Language:Chinese
-
Abstract:
To addresses the issues of low contrast and inaccurate segmentation of tiny vessels in retinal images,a U-shaped network incorporating multi-level residuals and three-space attention mechanism is proposed.In encoding stage,a multi-level residual module is added after inputting original images for preserving image features,and additionally,batch normalization and Dropout are integrated into the residual module to prevent vanishing gradient and feature data redundancy within the deep network.In decoding stage,a three-space attention mechanism is adopted to assign different weights to the features from the original images,down-sampled images,and up-sampled images,thus enhancing feature texture and position information,and achieving precise segmentation of tiny blood vessels.Experimental results on a public color fundus image dataset demonstrate that the proposed algorithm achieves higher accuracy(0.985),specificity(0.991),sensitivity(0.829),and AUC(0.985)than the existing algorithms.Moreover,the vessel maps obtained by the comparison with the gold standard are of significant reference value in clinic.
