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塞尔普-曼巴:通过选择性状态空间模型推进高分辨率视网膜血管细分.

Hongqiu Wang, Yixian Chen, Wu Chen

    IEEE transactions on medical imaging
    |June 30, 2025
    PubMed
    概括

    我们介绍了Serp-Mamba,这是一个用于在超广场扫描激光眼镜 (UWF-SLO) 图像中对视网膜血管进行细分的新型网络. 这种方法有效地处理高分辨率数据,并提高 fundus 疾病的诊断准确性.

    科学领域:

    • 眼科医生 眼科 眼科
    • 医疗成像医学成像
    • 计算机视觉 计算机视觉

    背景情况:

    • 准确的视网膜血管细分对于诊断底部疾病至关重要.
    • 超广场扫描激光眼镜 (UWF-SLO) 提供了广泛的视网膜视图,但由于高分辨率和复杂的血管结构,存在细分挑战.
    • 现有的方法在保护船舶连续性和解决UWF-SLO图像中的类不平衡方面扎.

    研究的目的:

    • 在高分辨率的UWF-SLO图像中开发一个先进的深度学习网络,用于精确的视网膜血管细分.
    • 为了利用Mamba的选择性状态空间模型 (SSM) 的优势,在血管结构中建模远程依赖.
    • 引入新的机制来处理船舶的复杂,曲线性质以及UWF-SLO图像中的显著类失衡.

    主要方法:

    • 提出了Serpentine Mamba (Serp-Mamba) 网络,将Mamba的SSM集成为高效的远程依赖模型.
    • 开发了一种蛇形交织自适应 (SIA) 扫描机制,可跟随曲的船体结构,确保持续的特征捕捉.
    • 引入了一个模糊性驱动的双重校准 (ADDR) 模块,通过改进模糊的像素划分来减轻类失衡.

    主要成果:

    • 塞尔普-马姆巴在三个独立数据集的高分辨率视网膜血管细分方面表现出卓越的性能.
    • 废弃性研究证实了SIA扫描和ADDR模块对网络有效性的重大贡献.

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  • 提出的方法成功地解决了与UWF-SLO成像固有的船舶连续性和类失衡相关的挑战.

    • 结论:

    • 塞尔普-曼巴网络为UWF-SLO图像中的视网膜血管细分提供了强大的和有效的解决方案.
    • SIA扫描和ADDR模块是提高细分精度和处理图像复杂性的关键创新.
    • 这项工作提升了使用高分辨率视网膜成像来自动检测和诊断 fundus 疾病的潜力.