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Machine Learning Approaches for Myocardial Motion and Deformation Analysis

Research output: Contribution to journalReview article

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Machine Learning Approaches for Myocardial Motion and Deformation Analysis. / Duchateau, Nicolas; King, Andrew P.; De Craene, Mathieu.

In: Frontiers in Cardiovascular Medicine, Vol. 6, 190, 09.01.2020.

Research output: Contribution to journalReview article

Harvard

Duchateau, N, King, AP & De Craene, M 2020, 'Machine Learning Approaches for Myocardial Motion and Deformation Analysis', Frontiers in Cardiovascular Medicine, vol. 6, 190. https://doi.org/10.3389/fcvm.2019.00190

APA

Duchateau, N., King, A. P., & De Craene, M. (2020). Machine Learning Approaches for Myocardial Motion and Deformation Analysis. Frontiers in Cardiovascular Medicine, 6, [190]. https://doi.org/10.3389/fcvm.2019.00190

Vancouver

Duchateau N, King AP, De Craene M. Machine Learning Approaches for Myocardial Motion and Deformation Analysis. Frontiers in Cardiovascular Medicine. 2020 Jan 9;6. 190. https://doi.org/10.3389/fcvm.2019.00190

Author

Duchateau, Nicolas ; King, Andrew P. ; De Craene, Mathieu. / Machine Learning Approaches for Myocardial Motion and Deformation Analysis. In: Frontiers in Cardiovascular Medicine. 2020 ; Vol. 6.

Bibtex Download

@article{af542defd16e4377b94a277ba26eb8dc,
title = "Machine Learning Approaches for Myocardial Motion and Deformation Analysis",
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.",
keywords = "cardiac imaging, computer-aided diagnosis, machine learning, myocardial motion, myocardial strain",
author = "Nicolas Duchateau and King, {Andrew P.} and {De Craene}, Mathieu",
year = "2020",
month = jan,
day = "9",
doi = "10.3389/fcvm.2019.00190",
language = "English",
volume = "6",
journal = "Frontiers in Cardiovascular Medicine",
issn = "2297-055X",
publisher = "Frontiers Media S.A.",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Machine Learning Approaches for Myocardial Motion and Deformation Analysis

AU - Duchateau, Nicolas

AU - King, Andrew P.

AU - De Craene, Mathieu

PY - 2020/1/9

Y1 - 2020/1/9

N2 - 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.

AB - 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.

KW - cardiac imaging

KW - computer-aided diagnosis

KW - machine learning

KW - myocardial motion

KW - myocardial strain

UR - http://www.scopus.com/inward/record.url?scp=85079385565&partnerID=8YFLogxK

U2 - 10.3389/fcvm.2019.00190

DO - 10.3389/fcvm.2019.00190

M3 - Review article

AN - SCOPUS:85079385565

VL - 6

JO - Frontiers in Cardiovascular Medicine

JF - Frontiers in Cardiovascular Medicine

SN - 2297-055X

M1 - 190

ER -

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