Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation : The MMs Challenge
Campello, Victor M. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Gkontra, Polyxeni (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Izquierdo, Cristian (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Martin-Isla, Carlos (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Sojoudi, Alireza (Circle Cardiovascular Imaging Pvt. Ltd.)
Full, Peter M. (German Cancer Research Center)
Maier-Hein, Klaus (Division of Medical Image Computing. German Cancer Research Center)
Zhang, Yao (Chinese Academy of Sciences. Institute of Computing Technology)
He, Zhiqiang (Lenovo Ltd.)
Ma, Jun (Nanjing University of Science and Technology)
Parreno, Mario (Universitat Politècnica de València)
Albiol, Alberto (Universitat Politècnica de València. iTeam Research Institute)
Kong, Fanwei (University of California at Berkeley. Department of Mechanical Engineering)
Shadden, Shawn C. (University of California at Berkeley. Department of Mechanical Engineeringy)
Corral Acero, Jorge (Institute of Biomedical Engineering. Department of Engineering Science. University of Oxford)
Sundaresan, Vaanathi (University of Oxford. Nuffield Department of Clinical Neurosciences)
Saber, Mina (Research and Development Division. Intixel Company S.A.E.)
Elattar, Mustafa (Research and Development Division. Intixel Company S.A.E.)
Li, Hongwei (Department of Computer Science. Technische Universität München)
Menze, Bjoern (Department of Computer Science. Technische Universität München)
Khader, Firas (ARISTRA GmbH)
Haarburger, Christoph (ARISTRA GmbH)
Scannell, Cian M. (School of Biomedical Engineering and Imaging Sciences. King's College London)
Veta, Mitko (Department of Biomedical Engineering. Eindhoven University of Technology)
Carscadden, Adam (Department of Radiology and Diagnostic Imaging. University of Alberta)
Punithakumar, Kumaradevan (Department of Radiology and Diagnostic Imaging. University of Alberta)
Liu, Xiao (School of Engineering. The University of Edinburgh)
Tsaftaris, Sotirios A. (School of Engineering. The University of Edinburgh)
Huang, Xiaoqiong (School of Biomedical Engineering. Shenzhen University)
Yang, Xin (School of Biomedical Engineering. Shenzhen University)
Li, Lei (School of Biomedical Engineering. Shenzhen University)
Zhuang, Xiahai (School of Data Science. Fudan University)
Viladés Medel, David (Institut d'Investigació Biomèdica Sant Pau)
Descalzo, Martin (Institut d'Investigació Biomèdica Sant Pau)
Guala, Andrea (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Mura, Lucía La (Department of Advanced Biomedical Sciences. University of Naples Federico II)
Friedrich, Matthias G. (Department of Medicine and Diagnostic Radiology. McGill University)
Garg, Ria (Department of Medicine and Diagnostic Radiology. McGill University)
Lebel, Julie (Department of Medicine and Diagnostic Radiology. McGill University)
Henriques, Filipe. (Department of Cardiology. University Heart Vascular Center Hamburg)
Karakas, Mahir (Department of Cardiology. University Heart Vascular Center Hamburg)
Cavus, Ersin (Barts Heart Centre. Barts Health NHS Trust)
Petersen, Steffen E. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Escalera, Sergio (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Segui, Santi (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Rodriguez-Palomares, Jose F.. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Lekadir, Karim (Universitat de Barcelona. Departament de Matemàtiques i Informàtica)
Universitat Autònoma de Barcelona

Data:	2021
Resum:	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 (MMs) 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.
Ajuts:	European Commission. Horizon 2020 825903
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Cardiovascular magnetic resonance ; Image segmentation ; Deep learning ; Generalizability ; Data augmentation ; Domain adaption ; Public dataset
Publicat a:	IEEE Transactions on Medical Imaging, Vol. 40 Núm. 12 (january 2021) , p. 3543-3554, ISSN 1558-254X

DOI: 10.1109/TMI.2021.3090082
PMID: 34138702

12 p, 2.9 MB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut de Recerca Sant Pau
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