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基于数字减法血管学,用于自动检测和定量动脉狭窄的CASMatching策略

Aziguli Wulamu¹, Jichang Luo², Saian Chen¹

¹Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, China; Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China.

Computer methods and programs in biomedicine

|November 5, 2023

概括

这项研究引入了一种新的深度学习方法,用于使用数字减去血管学精确定量动脉狭窄. 该方法准确地预测了关键的形态指数,改善了脑疾病的计算机辅助诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管诊断心血管诊断服务

背景情况:

动脉狭窄的诊断对于预防大脑疾病至关重要.
数字减去血管学 (DSA) 是黄金标准,但缺乏自动化定量分析.
来自DSA的狭窄检测和形态指数预测的深度学习是一个尚未解决的挑战.

研究的目的:

开发一种定量深度学习方法,用于预测动脉狭窄的形态指数.
为了使自动检测和精确量化DSA图像的动脉狭窄.

主要方法:

一个使用对象检测和回归模型的两阶段管道.
引入了动脉狭窄匹配 (CASMatching) 和用于标识血管段的匹配度得分.
开发了方向距离-IoU (2DIoU) 损失,以改善边界框回归.

主要成果:

通过拟议的物体探测器组件,改善了正常血管段的检测.
实现的平均绝对误差为RVD的0.378,MLD的0.221,狭窄度的4.9.
在来自三个不同X射线系统制造商的数据集上表现出强大的性能.

结论:

拟议的方法准确地定位了动脉狭窄和合适的正常血管段.
能够精确量化狭窄,为自动化分析提供一种新的解决方案.
为脑血管疾病的计算机辅助诊断系统提供了重大进展.

关键词:

冠状动脉狭窄症是什么深度学习是一种深度学习.数字减去血管学图.对象检测检测对象检测对象检测视觉回归是一种视觉回归.

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