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CVNet:基于深度学习的结膜血管的细分辅助激光诊断

Haoqian Lv, Yuqing Du, Ming Yang

|August 12, 2025

概括

一个新的模型,CVNet,准确地对结膜血管进行细分,用于非侵入性眼病查. 这种光学检测方法改善了实时监测和精确定位,用于先进的眼科检查.

科学领域:

眼科医生眼科眼科
生物医学工程生物医学工程
医疗成像医学成像

背景情况:

结膜血管的准确细分对于非侵入性光学检测和早期眼病查至关重要.
传统的细分方法缺乏复杂血管特征所需的精度,限制了诊断准确度.

研究的目的:

开发一个有效的结膜血管细分模型 (CVNet) 进行增强的非侵入性眼部监测.
提高眼镜成像中结膜血管检测的准确性和实时能力.

主要方法:

提出了CVNet,这是一种新型模型,具有双层嵌套U结构,并具有卷积块注意模块.
开发了一种用于收集结节血管图像的光学系统,并训练了CVNet模型.
使用CVNet细分结果实施了非侵入性光学检测实验.

主要成果:

在眼光图像上,CVNet实现了86.52%的细分性能,超过了现有网络.
该模型有效地捕捉了结膜血管的结构,纹理和对比特征.
证明了拉曼光谱系统的成功动态识别和精确坐标定位.

结论:

CVNet显著提高了结膜血管细分的准确性,用于非侵入性眼科诊断.
开发的光学系统和CVNet模型使实时眼睛检测和监控成为可能.
这种方法为早期眼病查和有针对性的光学干预提供了一个有希望的工具.

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