BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images

2025-09-27

AI Summary

Retinal vessel segmentation in optical coherence tomography angiography (OCTA) relies heavily on labor-intensive manual annotations, resulting in high costs and poor generalizability. Method: We propose BioVessel-Net, the first unsupervised generative framework for OCTA vessel segmentation. It explicitly encodes biostatistical vascular priors—including radius distribution and topological connectivity—via a vessel-radius-guided strategy and biostatistical consistency adversarial optimization, directly modeling anatomical structure rather than pixel-wise mapping. Additionally, we introduce RetinaMix, a high-resolution 2D/3D OCTA benchmark dataset. Contribution/Results: On RetinaMix and established benchmarks, BioVessel-Net achieves near-supervised segmentation accuracy (mean Dice ≥ 0.92), significantly outperforming state-of-the-art unsupervised, semi-supervised, and weakly supervised methods. Notably, it operates efficiently without GPU acceleration and offers strong interpretability through biologically grounded constraints.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

Abstract

Structural changes in retinal blood vessels are critical biomarkers for the onset and progression of glaucoma and other ocular diseases. However, current vessel segmentation approaches largely rely on supervised learning and extensive manual annotations, which are costly, error-prone, and difficult to obtain in optical coherence tomography angiography. Here we present BioVessel-Net, an unsupervised generative framework that integrates vessel biostatistics with adversarial refinement and a radius-guided segmentation strategy. Unlike pixel-based methods, BioVessel-Net directly models vascular structures with biostatistical coherence, achieving accurate and explainable vessel extraction without labeled data or high-performance computing. To support training and evaluation, we introduce RetinaMix, a new benchmark dataset of 2D and 3D OCTA images with high-resolution vessel details from diverse populations. Experimental results demonstrate that BioVessel-Net achieves near-perfect segmentation accuracy across RetinaMix and existing datasets, substantially outperforming state-of-the-art supervised and semi-supervised methods. Together, BioVessel-Net and RetinaMix provide a label-free, computationally efficient, and clinically interpretable solution for retinal vessel analysis, with broad potential for glaucoma monitoring, blood flow modeling, and progression prediction. Code and dataset are available: https://github.com/VikiXie/SatMar8.

Problem

• Unsupervised segmentation of retinal vessels from OCTA images
• Eliminating reliance on costly manual annotations for vessel analysis
• Providing clinically interpretable vessel extraction without labeled data

Innovation

• Unsupervised generative framework integrating vessel biostatistics
• Radius-guided segmentation strategy without manual annotations
• Label-free computationally efficient clinically interpretable vessel analysis