TY  - JOUR
AU  - Victor M. Campello
AU  - Polyxeni Gkontra
AU  - Cristian Izquierdo
AU  - Carlos Martin-Isla
AU  - Alireza Sojoudi
AU  - Peter M. Full
AU  - Klaus Maier-Hein
AU  - Yao Zhang
AU  - Zhiqiang He
AU  - Jun Ma
AU  - Mario Parreno
AU  - Alberto Albiol
AU  - Fanwei Kong
AU  - Shawn C. Shadden
AU  - Jorge Corral Acero
AU  - Vaanathi Sundaresan
AU  - Mina Saber
AU  - Mustafa Elattar
AU  - Hongwei Li
AU  - Bjoern Menze
AU  - Firas Khader
AU  - Christoph Haarburger
AU  - Cian M. Scannell
AU  - Mitko Veta
AU  - Adam Carscadden
AU  - Kumaradevan Punithakumar
AU  - Xiao Liu
AU  - Sotirios A. Tsaftaris
AU  - Xiaoqiong Huang
AU  - Xin Yang
AU  - Lei Li
AU  - Xiahai Zhuang
AU  - David Vilades
AU  - Martin L. Descalzo
AU  - Andrea Guala
AU  - Lucia La Mura
AU  - Matthias G. Friedrich
AU  - Ria Garg
AU  - Julie Lebel
AU  - Filipe Henriques
AU  - Mahir Karakas
AU  - Ersin Cavus
AU  - Steffen E. Petersen
AU  - Sergio Escalera
AU  - Santiago Segui
AU  - Jose F. Rodriguez Palomares
AU  - Karim Lekadir
PY  - 2021//
TI  - Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
T2  - TMI
JO  - IEEE Transactions on Medical Imaging
SP  - 3543
EP  - 3554
VL  - 40
IS  - 12
N2  - 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.
UR  - https://ieeexplore.ieee.org/document/9458279
UR  - http://dx.doi.org/10.1109/TMI.2021.3090082
N1  - HUPBA; no proj
ID  - Victor M. Campello2021
ER  -