TY  - JOUR
AU  - Carlos Martin Isla
AU  - Victor M Campello
AU  - Cristian Izquierdo
AU  - Kaisar Kushibar
AU  - Carla Sendra Balcells
AU  - Polyxeni Gkontra
AU  - Alireza Sojoudi
AU  - Mitchell J Fulton
AU  - Tewodros Weldebirhan Arega
AU  - Kumaradevan Punithakumar
AU  - Lei Li
AU  - Xiaowu Sun
AU  - Yasmina Al Khalil
AU  - Di Liu
AU  - Sana Jabbar
AU  - Sandro Queiros
AU  - Francesco Galati
AU  - Moona Mazher
AU  - Zheyao Gao
AU  - Marcel Beetz
AU  - Lennart Tautz
AU  - Christoforos Galazis
AU  - Marta Varela
AU  - Markus Hullebrand
AU  - Vicente Grau
AU  - Xiahai Zhuang
AU  - Domenec Puig
AU  - Maria A Zuluaga
AU  - Hassan Mohy Ud Din
AU  - Dimitris Metaxas
AU  - Marcel Breeuwer
AU  - Rob J van der Geest
AU  - Michelle Noga
AU  - Stephanie Bricq
AU  - Mark E Rentschler
AU  - Andrea Guala
AU  - Steffen E Petersen
AU  - Sergio Escalera
AU  - Jose F Rodriguez Palomares
AU  - Karim Lekadir
PY  - 2023//
TI  - Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge
T2  - JBHI
JO  - IEEE Journal of Biomedical and Health Informatics
SP  - 3302
EP  - 3313
VL  - 27
IS  - 7
N2  - 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.
UR  - https://pubmed.ncbi.nlm.nih.gov/37067963/
UR  - http://dx.doi.org/10.1109/JBHI.2023.3267857
N1  - HUPBA
ID  - Carlos Martin Isla2023
ER  -