PT Journal
AU 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 Beetz
   Lennart 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
TI Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge
SO IEEE Journal of Biomedical and Health Informatics
JI JBHI
PY 2023
BP 3302
EP 3313
VL 27
IS 7
DI 10.1109/JBHI.2023.3267857
AB 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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