基于Mamba的八分支网络，提升OCTA图像中视网膜血管与黄斑无灌注区分割精度
Joint-octamamba:an octa joint segmentation network based on feature enhanced mamba
- 融合多模块特征提取与Mamba状态空间模型，增强局部与全局特征表达
- 在OCTA-500数据集上，对视网膜血管和黄斑无灌注区的分割性能均优于现有方法
- 专为解决多任务分割中性能不平衡问题设计，适合眼科医学图像分析研究者
OCTA是一种关键的非侵入性成像技术，用于诊断和监测糖尿病视网膜病变、年龄相关性黄斑变性及青光眼等视网膜疾病。当前基于2D的方法在视网膜血管（RV）分割方面精度不足。为此，我们提出RVMamba，一种将多个特征提取模块与Mamba状态空间模型结合的新架构。此外，现有OCTA数据联合分割模型在不同任务间存在性能不平衡问题。为同时提升黄斑无灌注区（FAZ）分割效果并缓解此不平衡，我们引入FAZMamba及统一的Joint-OCTAMamba框架。在OCTA-500数据集上的实验结果表明，Joint-OCTAMamba在各项评估指标上均优于现有模型。代码已公开于https://github.com/lc-sfis/Joint-OCTAMamba。
原文摘要 · Abstract (English)
OCTA is a crucial non-invasive imaging technique for diagnosing and monitoring retinal diseases like diabetic retinopathy, age-related macular degeneration, and glaucoma. Current 2D-based methods for retinal vessel (RV) segmentation offer insufficient accuracy. To address this, we propose RVMamba, a novel architecture integrating multiple feature extraction modules with the Mamba state-space model. Moreover, existing joint segmentation models for OCTA data exhibit performance imbalance between different tasks. To simultaneously improve the segmentation of the foveal avascular zone (FAZ) and mitigate this imbalance, we introduce FAZMamba and a unified Joint-OCTAMamba framework. Experimental results on the OCTA-500 dataset demonstrate that Joint-OCTAMamba outperforms existing models across evaluation metrics.The code is available at https://github.com/lc-sfis/Joint-OCTAMamba.
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