Machine Learning Approaches for Myocardial Motion and Deformation Analysis.

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ID: 88261
2019
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Abstract
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.
Reference Key
duchateau2019machinefrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Duchateau, Nicolas;King, Andrew P;De Craene, Mathieu;
Journal Frontiers in cardiovascular medicine
Year 2019
DOI
10.3389/fcvm.2019.00190
URL
Keywords
Machine learning computer-aided diagnosis cardiac imaging myocardial motion myocardial strain

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