Work

EANet: Integrate Edge Features and Attention Mechanisms Multi‐Scale Networks for Vessel Segmentation in Retinal Images

Year: 2025

Type: article

Abstract: ABSTRACT Accurately extracting blood vessel structures from retinal fundus images is critical for the early diagnosis and treatment of various ocular and systemic diseases. However, retinal vessel segmentation continues to face significant challenges. Firstly, capturing the boundary information of small vessels is particularly difficult. Secondly, uneven vessel thickness and irregular distribution further complicate the multi‐scale feature modelling. Lastly, low‐contrast images lead to increased background noise, further affecting the segmentation accuracy. To tackle these challenges, this article presents a multi‐scale segmentation network that combines edge features and attention mechanisms, referred to as EANet. It demonstrates significant advantages over existing methods. Specifically, EANet consists of three key modules: the edge feature enhancement module, the multi‐scale information interaction encoding module, and the multi‐class attention mechanism decoding module. Experimental results validate the effectiveness of the method. Specifically, EANet outperforms existing advanced methods in the precise segmentation of small and multi‐scale vessels and in effectively filtering background noise to maintain segmentation continuity.

Source: IET Image Processing

DOI: 10.1049/ipr2.70056

Authors: Jiangyi Zhang, Yuxin Tan, Duantengchuan Li, Guanghui Xu, Fuling Zhou

Institutions: Wuhan University, Zhongnan Hospital of Wuhan University, Hubei University of Technology

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $2,740

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities