@Article{VictorM.Campello2021,
author="Victor M. Campello
and Polyxeni Gkontra
and Cristian Izquierdo
and Carlos Martin-Isla
and Alireza Sojoudi
and Peter M. Full
and Klaus Maier-Hein
and Yao Zhang
and Zhiqiang He
and Jun Ma
and Mario Parreno
and Alberto Albiol
and Fanwei Kong
and Shawn C. Shadden
and Jorge Corral Acero
and Vaanathi Sundaresan
and Mina Saber
and Mustafa Elattar
and Hongwei Li
and Bjoern Menze
and Firas Khader
and Christoph Haarburger
and Cian M. Scannell
and Mitko Veta
and Adam Carscadden
and Kumaradevan Punithakumar
and Xiao Liu
and Sotirios A. Tsaftaris
and Xiaoqiong Huang
and Xin Yang
and Lei Li
and Xiahai Zhuang
and David Vilades
and Martin L. Descalzo
and Andrea Guala
and Lucia La Mura
and Matthias G. Friedrich
and Ria Garg
and Julie Lebel
and Filipe Henriques
and Mahir Karakas
and Ersin Cavus
and Steffen E. Petersen
and Sergio Escalera
and Santiago Segui
and Jose F. Rodriguez Palomares
and Karim Lekadir",
title="Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M\&Ms Challenge",
journal="IEEE Transactions on Medical Imaging",
year="2021",
volume="40",
number="12",
pages="3543--3554",
abstract="The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M\&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3653), last updated on Thu, 16 Feb 2023 12:03:59 +0100",
doi="10.1109/TMI.2021.3090082",
opturl="https://ieeexplore.ieee.org/document/9458279"
}