Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/227789 |
| Acceso en línea: | https://hdl.handle.net/2445/227789 |
| Access Level: | acceso abierto |
| Palabra clave: | Imatges per ressonància magnètica Diagnòstic per la imatge Ventricles cardíacs Aprenentatge profund Magnetic resonance imaging Diagnostic imaging Ventricle of heart Deep learning (Machine learning) |
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Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms ChallengeVarela, MartaHüllebrand, MarkusGrau, VicenteZhuang, XiahaiPuig, DomènecZuluaga, Maria A.Mohy-ud-Din, HassanMetaxas, DimitrisBreeuwer, MarcelGeest, Rob J. van derLi, LeiNoga, MichelleSun, XiaowuBricq, StephanieAl Khalil, YasminaRentschler, Mark E.Liu, DiGuala, AndreaJabbar, SanaPetersen, Steffen E.Queiros, SandroEscalera Guerrero, SergioGalati, FrancescoRodriguez-Palomares, José F.Mazher, MoonaLekadir, Karim, 1977-Gao, ZheyaoBeetz, MarcelMartín Isla, CarlosCampello Román, Víctor ManuelIzquierdo, CristiánKushibar, K.Sendra-Balcells, C.Gkontra, PolyxeniSojoudi, A.Fulton, M.Weldebirhan, T.Punithakumar, K.Tautz, L.Galazis, C.Imatges per ressonància magnèticaDiagnòstic per la imatgeVentricles cardíacsAprenentatge profundMagnetic resonance imagingDiagnostic imagingVentricle of heartDeep learning (Machine learning)In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.Institute of Electrical and Electronics Engineers (IEEE)2026202620232026info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion12 p.application/pdfhttps://hdl.handle.net/2445/227789Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1109/JBHI.2023.3267857IEEE. Journal of Biomedical and Health Informatics, 2023, vol. 27, num.7, p. 3302-3313https://doi.org/10.1109/JBHI.2023.3267857(c) Institute of Electrical and Electronics Engineers (IEEE), 2023info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2277892026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| title |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| spellingShingle |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge Varela, Marta Imatges per ressonància magnètica Diagnòstic per la imatge Ventricles cardíacs Aprenentatge profund Magnetic resonance imaging Diagnostic imaging Ventricle of heart Deep learning (Machine learning) |
| title_short |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| title_full |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| title_fullStr |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| title_full_unstemmed |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| title_sort |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge |
| dc.creator.none.fl_str_mv |
Varela, Marta Hüllebrand, Markus Grau, Vicente Zhuang, Xiahai Puig, Domènec Zuluaga, Maria A. Mohy-ud-Din, Hassan Metaxas, Dimitris Breeuwer, Marcel Geest, Rob J. van der Li, Lei Noga, Michelle Sun, Xiaowu Bricq, Stephanie Al Khalil, Yasmina Rentschler, Mark E. Liu, Di Guala, Andrea Jabbar, Sana Petersen, Steffen E. Queiros, Sandro Escalera Guerrero, Sergio Galati, Francesco Rodriguez-Palomares, José F. Mazher, Moona Lekadir, Karim, 1977- Gao, Zheyao Beetz, Marcel Martín Isla, Carlos Campello Román, Víctor Manuel Izquierdo, Cristián Kushibar, K. Sendra-Balcells, C. Gkontra, Polyxeni Sojoudi, A. Fulton, M. Weldebirhan, T. Punithakumar, K. Tautz, L. Galazis, C. |
| author |
Varela, Marta |
| author_facet |
Varela, Marta Hüllebrand, Markus Grau, Vicente Zhuang, Xiahai Puig, Domènec Zuluaga, Maria A. Mohy-ud-Din, Hassan Metaxas, Dimitris Breeuwer, Marcel Geest, Rob J. van der Li, Lei Noga, Michelle Sun, Xiaowu Bricq, Stephanie Al Khalil, Yasmina Rentschler, Mark E. Liu, Di Guala, Andrea Jabbar, Sana Petersen, Steffen E. Queiros, Sandro Escalera Guerrero, Sergio Galati, Francesco Rodriguez-Palomares, José F. Mazher, Moona Lekadir, Karim, 1977- Gao, Zheyao Beetz, Marcel Martín Isla, Carlos Campello Román, Víctor Manuel Izquierdo, Cristián Kushibar, K. Sendra-Balcells, C. Gkontra, Polyxeni Sojoudi, A. Fulton, M. Weldebirhan, T. Punithakumar, K. Tautz, L. Galazis, C. |
| author_role |
author |
| author2 |
Hüllebrand, Markus Grau, Vicente Zhuang, Xiahai Puig, Domènec Zuluaga, Maria A. Mohy-ud-Din, Hassan Metaxas, Dimitris Breeuwer, Marcel Geest, Rob J. van der Li, Lei Noga, Michelle Sun, Xiaowu Bricq, Stephanie Al Khalil, Yasmina Rentschler, Mark E. Liu, Di Guala, Andrea Jabbar, Sana Petersen, Steffen E. Queiros, Sandro Escalera Guerrero, Sergio Galati, Francesco Rodriguez-Palomares, José F. Mazher, Moona Lekadir, Karim, 1977- Gao, Zheyao Beetz, Marcel Martín Isla, Carlos Campello Román, Víctor Manuel Izquierdo, Cristián Kushibar, K. Sendra-Balcells, C. Gkontra, Polyxeni Sojoudi, A. Fulton, M. Weldebirhan, T. Punithakumar, K. Tautz, L. Galazis, C. |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Imatges per ressonància magnètica Diagnòstic per la imatge Ventricles cardíacs Aprenentatge profund Magnetic resonance imaging Diagnostic imaging Ventricle of heart Deep learning (Machine learning) |
| topic |
Imatges per ressonància magnètica Diagnòstic per la imatge Ventricles cardíacs Aprenentatge profund Magnetic resonance imaging Diagnostic imaging Ventricle of heart Deep learning (Machine learning) |
| description |
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2026 2026 2026 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion |
| format |
article |
| status_str |
acceptedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2445/227789 |
| url |
https://hdl.handle.net/2445/227789 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Versió postprint del document publicat a: https://doi.org/10.1109/JBHI.2023.3267857 IEEE. Journal of Biomedical and Health Informatics, 2023, vol. 27, num.7, p. 3302-3313 https://doi.org/10.1109/JBHI.2023.3267857 |
| dc.rights.none.fl_str_mv |
(c) Institute of Electrical and Electronics Engineers (IEEE), 2023 info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
(c) Institute of Electrical and Electronics Engineers (IEEE), 2023 |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
12 p. application/pdf |
| dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
| publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
| dc.source.none.fl_str_mv |
Articles publicats en revistes (Matemàtiques i Informàtica) reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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1869406555465580544 |
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15,198674 |