OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

TL;DR

OCTAMamba is a novel U-shaped neural network that accurately segments retinal vasculature in OCTA images by combining multi-scale feature extraction and noise filtering, outperforming existing methods.

Contribution

The paper introduces OCTAMamba, a new deep learning architecture with specialized modules for precise OCTA vasculature segmentation, emphasizing efficiency and robustness.

Findings

OCTAMamba outperforms state-of-the-art segmentation methods on multiple datasets.

The model maintains linear complexity suitable for low-resource settings.

Extensive experiments validate the effectiveness of the proposed modules.

Abstract

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