Annotation-free 3D reconstruction and quantification of retinal microvasculature by RADAR.

ABSTRACT: Three-dimensional mapping of retinal microvasculature is essential for monitoring systemic vascular health. Existing methods rely heavily on manual annotation or training-intensive deep learning models that lack generalizability. Here we report RADAR. This is an annotation-free computational framework for the 3D segmentation and quantification of optical coherence tomography angiography data. The pipeline integrates adaptive physics-aware denoising with topology-preserving centerline extraction to reconstruct complex networks without manual labeling. We validated the framework in healthy individuals and patients with early-stage diabetic retinopathy. The method outperformed standard segmentation tools and resolved layer-specific morphological alterations obscured in conventional two-dimens

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12921226 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature