OCTAMamba用Mamba架构精准分割眼底血管，适合低算力医疗场景。
OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
- 基于Mamba架构的U型网络，融合多尺度与局部特征提取
- 在OCTA 3M、6M和ROSSA数据集上均超越现有方法
- 线性复杂度设计，适合边缘计算等资源受限场景
光学相干断层扫描血管成像（OCTA）是可视化视网膜血管、诊断糖尿病视网膜病变和青光眼等眼病的关键技术。然而，由于血管结构多尺度且图像质量差、眼部病变导致噪声大，精确分割仍具挑战。本文提出OCTAMamba，一种基于Mamba架构的新型U型网络，用于高精度OCTA血管分割。该模型集成四流高效挖掘嵌入模块以提取局部特征，多尺度空洞异构卷积模块捕捉多尺度血管结构，并引入聚焦特征重校准模块过滤噪声、突出目标区域。方法兼具高效全局建模与局部特征提取能力，保持线性复杂度，适用于低算力医疗应用。在OCTA 3M、OCTA 6M和ROSSA数据集上的大量实验表明，OCTAMamba优于当前先进方法，为高效OCTA分割提供了新参考。代码已开源：https://github.com/zs1314/OCTAMamba
原文摘要 · Abstract (English)
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
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