Duchateau N, King AP, De Craene M. Machine Learning Approaches for Myocardial Motion and Deformation Analysis.
Front Cardiovasc Med 2020;
6:190. [PMID:
31998756 PMCID:
PMC6962100 DOI:
10.3389/fcvm.2019.00190]
[Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 12/12/2019] [Indexed: 12/21/2022] Open
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.
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