摘要
随着心血管疾病介入治疗需求的持续增长,导管室内对术中医学影像的实时、精准分析已成为保障手术成功与患者安全的重要环节。传统影像判读在很大程度上依赖术者经验,存在主观性强、信息处理效率有限等问题。深度学习技术的快速发展为医学影像实时解析与辅助决策提供了新的解决方案。围绕导管室术中常见的透视影像(Fluoroscopy)、血管造影(DSA)、血流动力学数据等信息,构建了一套基于深度学习的实时分析与辅助决策模型体系,实现血管结构自动分割、病变识别、导管/器械定位及手术关键事件预测功能。实验结果表明,该系统能显著提升影像处理的速度和准确性,为介入手术的智能化发展奠定基础。
With the continuous growth of demand for interventional treatment of cardiovascular diseases,real-time and accurate analysis of surgical medical images in the catheterization room has become an important link to ensure surgical success and patient safety.Traditional image interpretation largely relies on the operator̓s experience,and has problems such as strong subjectivity and limited information processing efficiency.The rapid development of deep learning technology provides new solutions for real-time analysis and decision-making assistance of medical images.A real-time analysis and decision-making model system based on deep learning is constructed around common information such as fluoroscopy,angiography(DSA),and hemodynamic data during catheterization.This system achieves automatic segmentation of vascular structures,lesion recognition,catheter/instrument localization,and prediction of key surgical events.The experimental results show that the system can significantly improve the speed and accuracy of image processing,laying the foundation for the intelligent development of interventional surgery.
作者
罗士涛
严文丽
LUO Shitao;YAN Wenli(The People̓s Hospital of Hezhou,Hezhou,Guangxi 542800,China)
关键词
深度学习
导管室
医学影像
实时分析
辅助决策
介入手术
deep learning
catheter room
medical imaging
real-time analysis
assisted decision-making
interventional procedure
