首页> 外文会议>IEEE International Conference on Bioinformatics and Bioengineering >Determination of Image-based Biomarkers for the Diagnosis of Hypertrophic Cardiomyopathy, Hypertensive Cardiomyopathy and Amyloidosis From Texture Analysis in Cardiac MRI
【24h】

Determination of Image-based Biomarkers for the Diagnosis of Hypertrophic Cardiomyopathy, Hypertensive Cardiomyopathy and Amyloidosis From Texture Analysis in Cardiac MRI

机译:基于形象的生物标志物在心脏MRI纹理分析中诊断肥厚性心肌病变,高血压心肌病和淀粉样蛋白病

获取原文

摘要

Hypertrophic cardiomyopathy (HCM), hypertensive cardiomyopathy (HIP), and amyloidosis (AM) are pathologies in which a thickening of a portion of the myocardium occurs. All of them are manifested in a similar way on magnetic resonance images, which means that in most cases it is necessary to resort to the use of invasive diagnostic techniques. The objective of this work is to develop quantitative biomarkers that can differentiate between patients with these three pathologies using texture analysis on cardiac magnetic resonance imaging (MRI). In this study, a total of 103 patients underwent cine MRI. Two studies were carried out, one binary with patients with HCM and HIP and one multiclass considering the three pathologies. The left ventricular myocardium was segmented according to the standardized 17-segment model. A total of 43 features for each of the six segments were extracted using 5 different statistical methods. Four predictive models were implemented to evaluate the performance of the classification. Good precision results were obtained in both studies. For the binary study, a maximum AUC of $0.91pm 0.06$ was obtained with the K-Nearest Neighbours model and for the multiclass study the best performance (AUC $=0.89pm 0.12$) was achieved using the Support Vector Machine classifier.
机译:肥厚性心肌病(HCM),高血压心肌病(髋关节)和淀粉样症(AM)是病理的,其中发生一部分心肌的增厚。所有这些都以类似的方式表现在磁共振图像上,这意味着在大多数情况下,有必要采用侵入性诊断技术。这项工作的目的是开发能够在心脏磁共振成像(MRI)上的纹理分析中可以区分这些三种病理学患者的定量生物标志物。在这项研究中,共有103名患者接受了Cine MRI。进行两项研究,一个二进制与HCM患者和髋关节的患者和考虑到三种病理的多种多数。根据标准化的17段模型进行左心室心肌。使用5种不同的统计方法提取共六个段中的每一个的43个特征。实施了四种预测模型来评估分类的性能。两项研究中获得了良好的精度结果。对于二进制研究,使用K-Collect邻居模型获得了0.91 PM 0.06 $的最大AUC 0.06 $。使用支持向量机分类器实现最佳性能(AUC $ = 0.89 PM 0.12 $)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号