Duchateau Nicolas, King Andrew P, De Craene Mathieu
CREATIS, CNRS UMR 5220, INSERM U1206, Université, Lyon, France.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Front Cardiovasc Med. 2020 Jan 9;6:190. doi: 10.3389/fcvm.2019.00190. eCollection 2019.
Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.
心肌运动和变形的信息是区分正常和异常情况的关键。随着依赖数据而非预先设定模型的方法的出现,机器学习可以提高运动量化的稳健性,或者揭示区分病理状态的运动和变形模式(而非单个参数)。我们回顾了用于提取与运动相关描述符并在人群中分析此类特征的机器学习策略,同时牢记心脏应用特有的限制条件。