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基于MA-DUNetet的糖尿病视网膜血管细分算法

Jian-Zhi Deng^1,2, Yan Yang¹, Yong-Ping Guo¹

¹College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, China.

Quantitative imaging in medicine and surgery

|July 3, 2025

概括

这项研究引入了一种新的多模态注意力可变形U形网络 (MA-DUNet),用于增强视网膜血管细分,显著提高眼部疾病的诊断准确性.

科学领域:

医疗成像医学成像
眼科医生眼科眼科
计算机视觉计算机视觉

背景情况:

精确的视网膜血管细分对于诊断眼部疾病至关重要.
挑战包括复杂的分支,不同的直径,低对比度和微妙的终端血管.
准确的细分有助于早期诊断和治疗.

研究的目的:

为了提高视网膜血管细分的准确性.
通过改进细分来提高临床诊断的效率.

主要方法:

提出了多模式注意力可变形U型网络 (MA-DUNet).
集成的多尺度 (AMS) 卷用于多尺度的特征感知.
使用封闭通道转换 (GCT) 注意力机制进行特征传输.
在解码过程中使用多模态注意力融合块 (MAFB).

主要成果:

在DRIVE,STARE和CHASE-DB1数据集上实现了高精度 (95.72%-96.68%) 和ROC曲线下的面积 (98.10%-98.89%).
在医院验证的数据中表现优于比较模型.
证明了精细终端分支的优越细分和减少碎片化.

结论:

MA-DUNet模型提供了更准确的视网膜血管细分.
它有效地解决了精细的终端分支,模糊的边界和碎片化的挑战.
结果表明,血管细分的清晰度和诊断潜力有所提高.

关键词:

糖尿病人视网膜血管的血管.在MA-DUNet算法中,计算机辅助检测计算机辅助检测

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