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...

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Detalhes bibliográficos
Autores: 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.
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2023
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/227789
Acesso em linha:https://hdl.handle.net/2445/227789
Access Level:Acceso aberto
Palavra-chave:Imatges per ressonància magnètica
Diagnòstic per la imatge
Ventricles cardíacs
Aprenentatge profund
Magnetic resonance imaging
Diagnostic imaging
Ventricle of heart
Deep learning (Machine learning)
Descrição
Resumo: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.

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