@Article{CarlosMartinIsla2023,
author="Carlos Martin Isla
and Victor M Campello
and Cristian Izquierdo
and Kaisar Kushibar
and Carla Sendra Balcells
and Polyxeni Gkontra
and Alireza Sojoudi
and Mitchell J Fulton
and Tewodros Weldebirhan Arega
and Kumaradevan Punithakumar
and Lei Li
and Xiaowu Sun
and Yasmina Al Khalil
and Di Liu
and Sana Jabbar
and Sandro Queiros
and Francesco Galati
and Moona Mazher
and Zheyao Gao
and Marcel Beetz
and Lennart Tautz
and Christoforos Galazis
and Marta Varela
and Markus Hullebrand
and Vicente Grau
and Xiahai Zhuang
and Domenec Puig
and Maria A Zuluaga
and Hassan Mohy Ud Din
and Dimitris Metaxas
and Marcel Breeuwer
and Rob J van der Geest
and Michelle Noga
and Stephanie Bricq
and Mark E Rentschler
and Andrea Guala
and Steffen E Petersen
and Sergio Escalera
and Jose F Rodriguez Palomares
and Karim Lekadir",
title="Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M\&ms Challenge",
journal="IEEE Journal of Biomedical and Health Informatics",
year="2023",
volume="27",
number="7",
pages="3302--3313",
abstract="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{\textquoteright}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.",
optnote="HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3880), last updated on Fri, 12 Jan 2024 12:04:13 +0100",
doi="10.1109/JBHI.2023.3267857",
opturl="https://pubmed.ncbi.nlm.nih.gov/37067963/"
}