A retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion

被引：0
作者：
Li J. [1 ]
Gao G. [2 ]
Yang L. [2 ]
Liu Y. [2 ]
机构：
[1] College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan Province
[2] School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, Henan Province
来源：
Computers in Biology and Medicine | 2024年 / 172卷
基金：
中国国家自然科学基金;
关键词：
Deep learning; Dimensional attention; Edge enhancement; Retinal vessel segmentation;
D O I：
10.1016/j.compbiomed.2024.108315
中图分类号：
学科分类号：
摘要：
The incidence of blinding eye diseases is highly correlated with changes in retinal morphology, and is clinically detected by segmenting retinal structures in fundus images. However, some existing methods have limitations in accurately segmenting thin vessels. In recent years, deep learning has made a splash in the medical image segmentation, but the lack of edge information representation due to repetitive convolution and pooling, limits the final segmentation accuracy. To this end, this paper proposes a pixel-level retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion. Here, a multiple dimension attention enhancement (MDAE) block is proposed to acquire more local edge information. Meanwhile, a deep guidance fusion (DGF) block and a cross-pooling semantic enhancement (CPSE) block are proposed simultaneously to acquire more global contexts. Further, the predictions of different decoding stages are learned and aggregated by an adaptive weight learner (AWL) unit to obtain the best weights for effective feature fusion. The experimental results on three public fundus image datasets show that proposed network could effectively enhance the segmentation performance on retinal blood vessels. In particular, the proposed method achieves AUC of 98.30%, 98.75%, and 98.71% on the DRIVE, CHASE_DB1, and STARE datasets, respectively, while the F1 score on all three datasets exceeded 83%. The source code of the proposed model is available at https://github.com/gegao310/VesselSeg-Pytorch-master. © 2024 Elsevier Ltd
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