Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms challenge

Carlos Martin-Isla, Victor M. Campello, Cristian Izquierdo, Kaisar Kushibar, Carla Sendra-Balcells, Polyxeni Gkontra, Alireza Sojoudi, Mitchell J. Fulton, Tewodros Weldebirhan Arega, Kumaradevan Punithakumar, Lei Li, Xiaowu Sun, Yasmina Al Khalil, Di Liu, Sana Jabbar, Sandro Queiros, Francesco Galati, Moona Mazher, Zheyao Gao, Marcel BeetzLennart Tautz, Christoforos Galazis, Marta Varela, Markus Hullebrand, Vicente Grau, Xiahai Zhuang, Domenec Puig, Maria A. Zuluaga, Hassan Mohy-ud-Din, Dimitris Metaxas, Marcel Breeuwer, Rob J.van der Geest, Michelle Noga, Stephanie Bricq, Mark E. Rentschler, Andrea Guala, Steffen E. Petersen, Sergio Escalera, Jose F.Rodriguez Palomares, Karim Lekadir

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1 Citationer (Scopus)


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 12 th 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.

TidsskriftIEEE Journal of Biomedical and Health Informatics
Udgave nummer7
Sider (fra-til)3302-3313
Antal sider12
StatusUdgivet - jul. 2023

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