Abstract: To solve the problem of irregular and difficult segmentation of blood vessels in fundus retinal images, a multi-scale attention feature fusion network model based on a dual-decoder structure is proposed to achieve accurate segmentation of retinal blood vessels. The dual decoder branch network structure can reduce information loss. In the encoder, the multi-scale attention feature fusion module is designed to extract rich multi-scale features and the spatial attention module is combined to enhance the extraction of spatial context information and improve vascular recognition ability. Squeeze-and-excitation module is used to optimize aggregated features, suppress irrelevant feature channels and improve the comprehensive segmentation ability of the model. The experimental results on the DRIVE and CHASEDB1 data sets show that the recall rate can reach 0.841 1 and 0.855 1 respectively, making great progress compared with some advanced networks at present, with the maximum increase of 6.6% and 8.25% respectively.

Key words: medical image segmentation, retinal vessel segmentation, dual-decoder structure, multi-scale feature extraction, spatial attention module