Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/184038
Title: | Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
Author: | Campello, Víctor Manuel Gkontra, Polyxeni Izquierdo Morcillo, Cristian Martin-Isla, Carlos Sojoudi, Alireza Full, Peter M. Maier-Hein, Klaus Zhang, Yao He, Zhiqiang Ma, Jun Parreño, Mario Albiol, Alberto Kong, Fanwei Shadden, Shawn C. Acero Corral, Jorge Sundaresan, Vaanathi Saber, Mina Elattar, Mustafa Li, Hongwei Menze, Bjoern Khader, Firas Haarburger, Christoph Scannell, Cian M. Veta, Mitko Carscadden, Adam Punithakumar, Kumaradevan Liu, Xiao Tsaftaris, Sotirios A. Huang, Xiaoqiong Yang, Xin Li, Lei Zhuang, Xiahai Viladés, David Descalzo, Martín L. Guala, Andrea La Mura, Lucía Friedrich, Matthias G. Escalera Guerrero, Sergio Seguí Mesquida, Santi Lekadir, Karim, 1977- |
Keywords: | Aprenentatge automàtic Imatges per ressonància magnètica Processament digital d'imatges Machine learning Magnetic resonance imaging Digital image processing |
Issue Date: | 30-Nov-2021 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
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. |
Note: | Reproducció del document publicat a: https://doi.org/10.1109/TMI.2021.3090082 |
It is part of: | IEEE Transactions on Medical Imaging, 2021 |
URI: | http://hdl.handle.net/2445/184038 |
Related resource: | https://doi.org/10.1109/TMI.2021.3090082 |
ISSN: | 0278-0062 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) Publicacions de projectes de recerca finançats per la UE |
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