PT Journal
AU Victor M. Campello
   Polyxeni Gkontra
   Cristian Izquierdo
   Carlos Martin-Isla
   Alireza Sojoudi
   Peter M. Full
   Klaus Maier-Hein
   Yao Zhang
   Zhiqiang He
   Jun Ma
   Mario Parreno
   Alberto Albiol
   Fanwei Kong
   Shawn C. Shadden
   Jorge Corral Acero
   Vaanathi Sundaresan
   Mina Saber
   Mustafa Elattar
   Hongwei Li
   Bjoern Menze
   Firas Khader
   Christoph Haarburger
   Cian M. Scannell
   Mitko Veta
   Adam Carscadden
   Kumaradevan Punithakumar
   Xiao Liu
   Sotirios A. Tsaftaris
   Xiaoqiong Huang
   Xin Yang
   Lei Li
   Xiahai Zhuang
   David Vilades
   Martin L. Descalzo
   Andrea Guala
   Lucia La Mura
   Matthias G. Friedrich
   Ria Garg
   Julie Lebel
   Filipe Henriques
   Mahir Karakas
   Ersin Cavus
   Steffen E. Petersen
   Sergio Escalera
   Santiago Segui
   Jose F. Rodriguez Palomares
   Karim Lekadir
TI Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
SO IEEE Transactions on Medical Imaging
JI TMI
PY 2021
BP 3543
EP 3554
VL 40
IS 12
DI 10.1109/TMI.2021.3090082
AB 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.
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