A precise image-based retinal blood vessel segmentation method using TAOD-CFNet

Abstract

In 2013, an estimated 64 million people between the ages of 40 and 80 were suffering from eye disease. By 2020, that number had climbed to 76 million. It is predicted that by 2040, there will be a staggering 111.8 million glaucoma patients worldwide. Segmentation of blood vessels in retinal images can be used to investigate many diseases, and the complexity of the blood vessels and the variable conditions inside the retina pose a high challenge for accurate segmentation. Therefore, a competing fusion segmentation network (TAOD −CFNet) with a trumpet-like attention mechanism and optic disc gradient adjustment algorithm for retinal blood vessel segmentation. First, an optic disc gradient adjustment algorithm (ODGA) is proposed, which designs dual threshold weights for accurate localization and optimization of optic disc areas. Then, a competing fusion block (CFB) is proposed to improve the feature dissimilarity between the arteriovenous vascular sensitive area and the interference area. Finally, a Trumpet Attention Mechanism (TAM) is proposed to enhance the edge features of fine and peripheral blood vessels. TAOD-CFNet outperforms ten SOTA methods in ten-fold cross-validation, with IOU, F1-Score, Dice, Jaccard, ACC and MCC metrics reaching 83.28%, 89.41%, 84.28%, 80.35%, 96.94% and 88.81%. To demonstrate the generalization performance of the model, TAOD-CFNet outperforms ten SOTA image segmentation methods on six retinal image datasets (DRIVE, CHASEDB1, STARE, HRF, IOSTAR, and LES). The experimental results proved that TAOD-CFNet possesses better segmentation performance and can assist clinicians in determining the condition of retinopathy patients.