基于超声弹性成像的支持向量机对颈动脉易损斑块的自动识别  

Ultrasound Elastography Based SVM for Automatic Identification of Carotid Vulnerable Plaques

作　　者：徐游民 刘志 何琼[1] 罗建文[1] XU Youmin;LIU Zhi;HE Qiong;LUO Jianwen(College of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China)

机构地区：[1]清华大学医学院生物医学工程系

出　　处：《中国医疗设备》2019年第5期15-19,共5页China Medical Devices

基　　金：国家重点研发计划(2016YFC0102200)

摘　　要：本研究的目的是使用基于超声弹性成像的支持向量机(Support Vector Machine,SVM)实现对颈动脉易损斑块的自动检测。共采集了52个志愿者的80例颈动脉粥样硬化斑块的超声长轴射频数据,利用弹性成像算法得到应变率分布,并提取应变率相关特征;同时,根据高分辨率磁共振成像诊断结果,将斑块分为稳定斑块和易损斑块。根据受试者工作特征曲线下的面积对各个特征进行分析,最后选取绝对应变率的99%分位数、最大值、标准差和均值等四个特征,并进行组合,采用径向基函数为核函数的SVM对颈动脉易损斑块进行识别,在测试集上的灵敏性、特异性、准确性分别为70.0%、88.0%、81.3%。本研究初步验证了基于超声弹性成像的SVM在颈动脉易损斑块自动识别中的可行性。The objective of this study was automatic identification of carotid vulnerable plaques using ultrasound elastography based support vector machine(SVM). Ultrasound radiofrequency data of 80 carotid atherosclerotic plaques from 52 volunteers were acquired in the longitudinal view, and were used to estimate the strain rate distribution with an elastography algorithm.Then the strain rate features of the plaques were extracted. Meanwhile, the plaques were classified to be stable or vulnerable using high-resolution magnetic resonance imaging. The area under the receiver operating characteristic curve was used to analyze each strain rate feature, and the maximum, 99 th percentile, mean, and standard deviation of absolute strain rates were selected and combined. The vulnerable plaques were identified using SVM with radial basis function, achieving sensitivity,specificity, and accuracy of 70.0%, 88.0%, and 81.3%, respectively, in the testing dataset. This study validates the feasibility of ultrasound elastography based SVM in automatic identification of carotid vulnerable plaques.

关 键 词：支持向量机 自动识别 颈动脉粥样硬化斑块 超声弹性成像 应变率 易损性 

分 类 号：R312[医药卫生—基础医学]