Enhancing Retinal Vessel Segmentation with a Simplified nnU-Net Model and CBAM

Abstract

To better diagnose and monitor ocular diseases, segmentation of blood vessels on the retina is crucial, but manual annotation is labor-intensive and error-prone. To deal with these problems, many automatic segmentation algorithms have already been developed, with deep learning techniques, leading the advancements. This study aims to enhance retinal vessel segmentation by refining the no-new-U-Net (nnU-Net) model through two key modifications: the integration of the Convolutional Block Attention Module (CBAM) attention blocks and the replacement of the original Stage 1 nnU-Net with a simplified Residual Neural Network (ResNet)-like network. The incorporation of CBAM improves feature extraction by focusing on the most relevant features, while the ResNet-like architecture simplifies the network structure, making it more efficient. Experimental results on the Retina Blood Vessel Dataset reveal that the modified nnU-Net model significantly outperforms the original in terms of both Dice score and prediction time. Specifically, it achieves superior accuracy in identifying fine blood vessels and demonstrates reduced memory usage, showcasing its potential for delivering more precise and efficient retinal vessel segmentation.