用于视网膜血管细分的层次式多尺度mamba与管状结构意识的卷积
Tao Wang1,2, Dongyuan Tian1, Haonan Zhao3
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
HM-Mamba是一种新的深度学习模型, 增强了视网膜血管细分, 在视网膜图像中精确识别血管, 提高诊断能力.
科学领域:
- 医学成像
- 计算机视觉
- 生物医学工程
背景情况:
- 视网膜血管细分对于诊断视网膜和心血管疾病至关重要.
- 挑战包括 fundus 图像中的照明变化和噪音,影响容器的完整性.
- 准确的细分是计算机辅助诊断系统的基础.
研究的目的:
- 介绍HM-Mamba,一种基于Mamba的多层次架构,用于改善视网膜血管细分.
- 通过结合管状结构感知卷积来增强局部和全球血管特征的提取.
- 提高细分复杂血管结构的稳定性和准确性.
主要方法:
- 开发了一种管状结构意识的卷积, 以保持血管的连续性和完整性.
- 设计了一个多尺度的融合模块,
- 集成的多分支福里埃转换与Mamba用于远程依赖和多频率信息捕获.
- 为有效的多层次语义融合提出了层次化的多层次交互式Mamba块.
主要成果:
- 在五个基准数据集 (DRIVE,CHASE_DB1,STARE,IOSTAR,LES-AV) 上显示出优异的性能.
- 获得了高的子系数:0.8327 (驱动),0.8197 (CHASE_DB1),0.8239 (STARE),0.8307 (IOSTAR) 和0.8426 (LES-AV).
- 该架构有效地处理复杂的空间模式,并减少在下方采样过程中的细节损失.
结论:
- HM-Mamba为视网膜血管细分提供了强大而有效的解决方案.
- 提出的方法显著改善了血管结构的表现,包括细分支.
- 这一进步有望提高眼睛和心血管疾病的计算机辅助诊断系统的准确性和可靠性.
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