DBMAE-Net: A dual branch multi-scale feature adaptive extraction network for retinal arteriovenous vessel segmentation

Abstract

Vessel morphology and tube diameter changes in the retina are closely related to diseases such as diabetic retinopathy and glaucoma. Traditional methods of vessel segmentation and tube diameter measurement suffer from insufficient accuracy and low automation. To address these limitations, this paper proposes a retinal arteriovenous segmentation network model based on dual branch multi-scale feature adaptive extraction. The model is designed to tackle the challenge of complex and similar arteriovenous vessel structures by incorporating a multi-scale feature extraction and fusion module, which enhances the capture of contextual vessel information and microvascular features. To ensure the model learns rich semantic information, a dual-branch encoder is implemented to extract multi-scale and residual features. The multi-feature local global attention fusion module then performs feature fusion, integrating these features effectively. In the down-sampling module, an adaptive pooling mechanism is introduced to balance detailed and global information of the arterial vessels. This adaptive learning of hyperparameters helps to mitigate the confusion between arterial and venous vessels. Furthermore, a multi-layer semantic supervision module is incorporated to enhance the model’s learning ability and improve segmentation accuracy for arteriovenous vessel features. The method’s effectiveness is validated on the DRIVE-AV, HRF-AV, and LES-AV datasets, achieving accuracy rates of 94.19%, 99.26%, and 93.10%, respectively. Additionally, an equivalent diameter measurement method based on optic disc and macular localization is proposed. This method accurately calculates the equivalent diameter of arterioles and veins, providing quantitative indices for the auxiliary diagnosis of related diseases and enhancing clinical efficiency.