An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation

2026-09-11

AI Summary

为解决超宽视野SS-OCTA视网膜血管分割缺乏基准的问题，研究引入了首个公开数据集WOIVES，并提出了一种新的模型PG-Mamba以提高分割精度。

Abstract

Ultra-widefield (UWF) swept-source optical coherence tomography angiography (SS-OCTA) enables large-area retinal vascular imaging, yet vessel segmentation at this scale lacks dedicated public benchmarks and comprehensive evaluation for quantitative vascular analysis. We introduce WOIVES, to our knowledge the first publicly available UWF SS-OCTA vessel-segmentation dataset, comprising 206 eyes from 152 participants with a 24x20mm^2 field of view. WOIVES spans emmetropia to high myopia and provides soft probability vessel annotations. We further propose PG-Mamba, a visual state space model that enhances conventional directional scans with two complementary polar-coordinate scan orders. An auxiliary Dynamic FOV Gating module performs spatial modulation at the bottleneck. PG-Mamba outperformed seven competitive approaches on broad segmentation metrics under cross-validation. It achieved the lowest median absolute errors for vessel density, fractal dimension, and vessel length density. WOIVES is publicly available on Zenodo (DOI: 10.5281/zenodo.21904672), and the PG-Mamba code is available at https://github.com/syb1234567/PG-Mamba.

Problem

• Ultra-Widefield
• Swept-Source OCTA
• Vessel Segmentation
• Public Benchmarks
• Quantitative Vascular Analysis

Innovation

• Ultra-Widefield
• Swept-Source OCTA
• Vessel Segmentation
• Polar-Gated Mamba Network
• Dynamic FOV Gating