CALIBER: Conditional Adversarial Learning for Intelligent Retinal Vessel Segmentation and Quantitative Biomarker Extraction

The examination of the retinal vasculature through fundus images provides a direct insight into vessel morphology, which is crucial in the diagnosis of the leading causes of blindness, such as glaucoma, diabetic retinopathy (DR), and age-related macular degeneration (AMD). However, the objective and quantitative interpretation of the retinal vasculature is not possible without a precise vessel segmentation. This paper introduces Conditional Adversarial Learning for Intelligent Retinal Vessel Segmentation and Quantitative Biomarker Extraction (CALIBER), a new deep-learning-based framework that unifies high-precision retinal vessel segmentation with automated vascular biomarker extraction and quantification. The segmentation module adapts a conditional generative adversarial network and is validated across six public datasets, achieving accuracy rates between 0.925 and 0.977, and continuous ROC-AUC scores ranging from 0.765 to 0.979. Using the segmented vasculature, we extract eight quantitative biomarkers and demonstrate significant morphological differences between healthy subjects and individuals with glaucoma, DR, and AMD. We establish exploratory out-of-sample diagnostic decision thresholds, specifically identifying fractal dimensions of <1.55 for glaucoma and AMD, and a tortuosity of >1.08 with branching angles of <87∘ for advanced DR, confirming that quantitative vascular phenotyping is viable through automated segmentation-based analysis. Understanding these early changes holds promise for the development of effective treatment and blindness prevention protocols. Accordingly, CALIBER merges as a valuable tool in ophthalmic diagnostics by jointly enabling a precise vessel segmentation and a biomarker extraction for the early detection of vascular changes.