Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts

Gerardo Kenny Rumindo; Nicolas Duchateau; Pierre Croisille; Jacques Ohayon; Patrick Clarysse

doi:10.1007/978-3-319-59448-4_11

Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts

Gerardo Kenny Rumindo, Nicolas Duchateau, Pierre Croisille, Jacques Ohayon, Patrick Clarysse

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Localization of infarcted regions is essential to determine the most appropriate treatment for patients with cardiac ischemia. Myocardial strain partially reflects the location of infarcted regions, which demonstrated potential use in clinical practice. However, strain patterns are complex and simple thresholding is not sufficient to locate the infarcts. Besides, many strain-based parameters exist and their sensitivities to myocardial infarcts have not been directly investigated. In our study, we propose to evaluate nine strain-based parameters to locate infarcted regions. For this purpose, we designed a large database (n = 200) of synthetic pathological finite-element heart models from 5 real healthy left ventricle geometries. The infarcts were incorporated with random location, shape and degree of severity. In addition, we used a state-of-the-art learning algorithm to link deformation patterns and infarct location. Based on our evaluation, we propose to sort the strain-based parameters into three groups according to their performances in locating infarcts.

Original language	English (US)
Title of host publication	Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
Editors	Mihaela Pop, Graham A. Wright
Publisher	Springer-Verlag
Pages	106-114
Number of pages	9
ISBN (Print)	9783319594477
DOIs	https://doi.org/10.1007/978-3-319-59448-4_11
State	Published - 2017
Event	9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017 - Toronto, Canada Duration: Jun 11 2017 → Jun 13 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10263 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017
Country/Territory	Canada
City	Toronto
Period	6/11/17 → 6/13/17

Keywords

Finite-element model
Infarct diagnosis
Machine learning
Myocardial infarct
Myocardial strain

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-59448-4_11

Cite this

Rumindo, G. K., Duchateau, N., Croisille, P., Ohayon, J., & Clarysse, P. (2017). Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts. In M. Pop, & G. A. Wright (Eds.), Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings (pp. 106-114). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10263 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-59448-4_11

Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts. / Rumindo, Gerardo Kenny; Duchateau, Nicolas; Croisille, Pierre et al.
Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. ed. / Mihaela Pop; Graham A. Wright. Springer-Verlag, 2017. p. 106-114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10263 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rumindo, GK, Duchateau, N, Croisille, P, Ohayon, J & Clarysse, P 2017, Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts. in M Pop & GA Wright (eds), Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10263 LNCS, Springer-Verlag, pp. 106-114, 9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017, Toronto, Canada, 6/11/17. https://doi.org/10.1007/978-3-319-59448-4_11

Rumindo GK, Duchateau N, Croisille P, Ohayon J, Clarysse P. Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts. In Pop M, Wright GA, editors, Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. Springer-Verlag. 2017. p. 106-114. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59448-4_11

Rumindo, Gerardo Kenny ; Duchateau, Nicolas ; Croisille, Pierre et al. / Strain-based parameters for infarct localization : Evaluation via a learning algorithm on a synthetic database of pathological hearts. Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings. editor / Mihaela Pop ; Graham A. Wright. Springer-Verlag, 2017. pp. 106-114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Localization of infarcted regions is essential to determine the most appropriate treatment for patients with cardiac ischemia. Myocardial strain partially reflects the location of infarcted regions, which demonstrated potential use in clinical practice. However, strain patterns are complex and simple thresholding is not sufficient to locate the infarcts. Besides, many strain-based parameters exist and their sensitivities to myocardial infarcts have not been directly investigated. In our study, we propose to evaluate nine strain-based parameters to locate infarcted regions. For this purpose, we designed a large database (n = 200) of synthetic pathological finite-element heart models from 5 real healthy left ventricle geometries. The infarcts were incorporated with random location, shape and degree of severity. In addition, we used a state-of-the-art learning algorithm to link deformation patterns and infarct location. Based on our evaluation, we propose to sort the strain-based parameters into three groups according to their performances in locating infarcts.",

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AU - Rumindo, Gerardo Kenny

AU - Duchateau, Nicolas

AU - Croisille, Pierre

AU - Ohayon, Jacques

AU - Clarysse, Patrick

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Localization of infarcted regions is essential to determine the most appropriate treatment for patients with cardiac ischemia. Myocardial strain partially reflects the location of infarcted regions, which demonstrated potential use in clinical practice. However, strain patterns are complex and simple thresholding is not sufficient to locate the infarcts. Besides, many strain-based parameters exist and their sensitivities to myocardial infarcts have not been directly investigated. In our study, we propose to evaluate nine strain-based parameters to locate infarcted regions. For this purpose, we designed a large database (n = 200) of synthetic pathological finite-element heart models from 5 real healthy left ventricle geometries. The infarcts were incorporated with random location, shape and degree of severity. In addition, we used a state-of-the-art learning algorithm to link deformation patterns and infarct location. Based on our evaluation, we propose to sort the strain-based parameters into three groups according to their performances in locating infarcts.

AB - Localization of infarcted regions is essential to determine the most appropriate treatment for patients with cardiac ischemia. Myocardial strain partially reflects the location of infarcted regions, which demonstrated potential use in clinical practice. However, strain patterns are complex and simple thresholding is not sufficient to locate the infarcts. Besides, many strain-based parameters exist and their sensitivities to myocardial infarcts have not been directly investigated. In our study, we propose to evaluate nine strain-based parameters to locate infarcted regions. For this purpose, we designed a large database (n = 200) of synthetic pathological finite-element heart models from 5 real healthy left ventricle geometries. The infarcts were incorporated with random location, shape and degree of severity. In addition, we used a state-of-the-art learning algorithm to link deformation patterns and infarct location. Based on our evaluation, we propose to sort the strain-based parameters into three groups according to their performances in locating infarcts.

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Strain-based parameters for infarct localization: Evaluation via a learning algorithm on a synthetic database of pathological hearts

Abstract

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Keywords

ASJC Scopus subject areas

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