OCTAMamba：一种用于精确OCTA血管分割的状态空间模型方法
OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
AI总结：
本文提出基于Mamba架构的U形网络OCTAMamba，通过多模块设计实现多尺度OCTA血管精确分割，在多个数据集上超越现有方法并保持线性复杂度。
摘要：
光学相干断层扫描血管成像（OCTA）是一种用于可视化视网膜血管系统以及诊断糖尿病视网膜病变和青光眼等眼部疾病的关键成像技术。然而，由于多尺度血管结构以及图像质量差和眼部病变带来的噪声，OCTA血管的精确分割仍然具有挑战性。在本研究中，我们提出了OCTAMamba，这是一种基于Mamba架构的新型U形网络，旨在精确分割OCTA中的血管系统。OCTAMamba集成了用于局部特征提取的Quad Stream Efficient Mining Embedding Module、用于捕获多尺度血管的Multi-Scale Dilated Asymmetric Convolution Module，以及用于过滤噪声并突出目标区域的Focused Feature Recalibration Module。我们的方法在保持线性复杂度的同时实现了高效的全局建模和局部特征提取，使其适用于低计算资源的医学应用。在OCTA 3M、OCTA 6M和ROSSA数据集上的大量实验表明，OCTAMamba优于最先进的方法，为高效OCTA分割提供了新的参考。代码可在https://github.com/zs1314/OCTAMamba获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
