OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
This work addresses the problem of accurate OCTA vasculature segmentation for diagnosing eye diseases like diabetic retinopathy and glaucoma, representing an incremental improvement with a new method for a known bottleneck.
The study tackled the challenge of precise segmentation of retinal vasculature in OCTA images, which is hindered by multi-scale structures and noise, by proposing OCTAMamba, a novel U-shaped network based on the Mamba architecture, achieving state-of-the-art performance on multiple datasets.
Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://github.com/zs1314/OCTAMamba