Ultra-widefield octa dataset and network improve retinal vessel segmentation
An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation
Computer Vision and Pattern Recognition
Summary
Retinal scans cover large areas but lack good public data for analyzing blood vessels accurately. The authors created WOIVES, a new large set of retinal images with detailed vessel markings to help this problem. They also developed PG-Mamba, a model that looks at the images from different angles to better find blood vessels. This model worked better than others in measuring blood vessel features precisely.
What this means in practice
- •For medical imaging teams: Use the WOIVES dataset and PG-Mamba model to improve automated retinal vessel analysis at large imaging scales.
- •For ophthalmology software developers: Integrate PG-Mamba’s multi-angle scanning approach to enhance vessel segmentation accuracy in retinal diagnostic tools.$Commercial implications: This paper enables advanced retinal analysis software capable of better vessel detection for eye care providers.
Authors
Yang Liu, Yibing Shen, Keming Zhao, Cenk Jiang, Zhenghang Qian, Zhicheng Du, Chen Xiong, Qidong Shao, Zijun Lin, Yunqi Hu, Jingjing Zhou, Lian Zhang, Peter E. Lobie, Peiwu Qin, Chengming Yang
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.