Deep Learning-based Automated Segmentation of Ultra-Widefield Retinal Vasculature for Cardiometabolic Disease Association Analysis.
Retinal vessel analysis in ultra-widefield (UWF) images provides a unique opportunity for large-scale assessment of systemic microvascular health. However, accurate segmentation in true-color UWF images remains challenging due to the large field of view, complex background, and reduced vessel contrast. To address these challenges, we develop ECS-Net, a dedicated deep learning (DL) framework for retinal vessel segmentation in true-color UWF images. Our ECS-Net adopts an enhanced encoder decoder architecture that integrates a dual-domain context enhancement module (DCEM), consisting of an anisotropic spatial focus unit (ASFU) and a feature correlation calibration unit (FCCU), together with atrous spatial pyramid pooling (ASPP) for multi-scale context modeling. The proposed ECS Net achieved a higher Dice coefficient of 0.8349 than other state-of-the-art algorithms (0.7001-0.8136), demonstrating strong generalization under real-world imaging conditions. Building upon accurate vessel extraction, region-specific vascular parameters, including vessel density (VD), fractal dimension (FD), tortuosity (TC), and mean curvature (MC), were quantified separately for central (45°), peripheral (45° 133°), and global (133°) zones. Multivariable logistic regression was used to evaluate associations with metabolic diseases in 4,618 participants. Hypertension showed significant inverse associations with VD and FD across all zones (all P < 0.001). Diabetes exhibited striking regional specificity, characterized by increased TC and MC and decreased FD were confined to the peripheral and global zones (all P< 0.01), with inverse associations for TC and MC detectable only in the global zone (P < 0.05). This study establishes the first DL framework specifically designed for retinal vessel segmentation in true color UWF images and reveals disease-specific regional vascular patterns that would be missed by conventional fundus photography, highlighting the value of UWF imaging for comprehensive systemic disease assessment and providing a biological prior for the development of interpretable and region-aware models. The code will be released on GitHub: https://github.com/yxyXinyue/UWF Retinal-Vasculature-Segmentation-Cardiometabolic.