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

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.