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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. / Campello, Victor M; Gkontra, Polyxeni; Izquierdo, Cristian; Martin-Isla, Carlos; Sojoudi, Alireza; Full, Peter M; Maier-Hein, Klaus; Zhang, Yao; He, Zhiqiang; Ma, Jun; Parreno, Mario; Albiol, Alberto; Kong, Fanwei; Shadden, Shawn C; Acero, Jorge Corral; 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; Vilades, David; Descalzo, Martin L; Guala, Andrea; Mura, Lucia La; Friedrich, Matthias G; Garg, Ria; Lebel, Julie; Henriques, Filipe; Karakas, Mahir; Cavus, Ersin; Petersen, Steffen E; Escalera, Sergio; Segui, Santi; Rodriguez-Palomares, Jose F; Lekadir, Karim.

in: IEEE T MED IMAGING, Jahrgang 40, Nr. 12, 12.2021, S. 3543-3554.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Campello, VM, Gkontra, P, Izquierdo, C, Martin-Isla, C, Sojoudi, A, Full, PM, Maier-Hein, K, Zhang, Y, He, Z, Ma, J, Parreno, M, Albiol, A, Kong, F, Shadden, SC, Acero, JC, Sundaresan, V, Saber, M, Elattar, M, Li, H, Menze, B, Khader, F, Haarburger, C, Scannell, CM, Veta, M, Carscadden, A, Punithakumar, K, Liu, X, Tsaftaris, SA, Huang, X, Yang, X, Li, L, Zhuang, X, Vilades, D, Descalzo, ML, Guala, A, Mura, LL, Friedrich, MG, Garg, R, Lebel, J, Henriques, F, Karakas, M, Cavus, E, Petersen, SE, Escalera, S, Segui, S, Rodriguez-Palomares, JF & Lekadir, K 2021, 'Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge', IEEE T MED IMAGING, Jg. 40, Nr. 12, S. 3543-3554. https://doi.org/10.1109/TMI.2021.3090082

APA

Campello, V. M., Gkontra, P., Izquierdo, C., Martin-Isla, C., Sojoudi, A., Full, P. M., Maier-Hein, K., Zhang, Y., He, Z., Ma, J., Parreno, M., Albiol, A., Kong, F., Shadden, S. C., Acero, J. C., Sundaresan, V., Saber, M., Elattar, M., Li, H., ... Lekadir, K. (2021). Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE T MED IMAGING, 40(12), 3543-3554. https://doi.org/10.1109/TMI.2021.3090082

Vancouver

Campello VM, Gkontra P, Izquierdo C, Martin-Isla C, Sojoudi A, Full PM et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE T MED IMAGING. 2021 Dez;40(12):3543-3554. https://doi.org/10.1109/TMI.2021.3090082

Bibtex

@article{6c7cc43c5396472ea683017786d385fd,
title = "Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge",
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.",
keywords = "Cardiac Imaging Techniques, Heart/diagnostic imaging, Humans, Magnetic Resonance Imaging",
author = "Campello, {Victor M} and Polyxeni Gkontra and Cristian Izquierdo and Carlos Martin-Isla and Alireza Sojoudi and Full, {Peter M} and Klaus Maier-Hein and Yao Zhang and Zhiqiang He and Jun Ma and Mario Parreno and Alberto Albiol and Fanwei Kong and Shadden, {Shawn C} and Acero, {Jorge Corral} and Vaanathi Sundaresan and Mina Saber and Mustafa Elattar and Hongwei Li and Bjoern Menze and Firas Khader and Christoph Haarburger and Scannell, {Cian M} and Mitko Veta and Adam Carscadden and Kumaradevan Punithakumar and Xiao Liu and Tsaftaris, {Sotirios A} and Xiaoqiong Huang and Xin Yang and Lei Li and Xiahai Zhuang and David Vilades and Descalzo, {Martin L} and Andrea Guala and Mura, {Lucia La} and Friedrich, {Matthias G} and Ria Garg and Julie Lebel and Filipe Henriques and Mahir Karakas and Ersin Cavus and Petersen, {Steffen E} and Sergio Escalera and Santi Segui and Rodriguez-Palomares, {Jose F} and Karim Lekadir",
year = "2021",
month = dec,
doi = "10.1109/TMI.2021.3090082",
language = "English",
volume = "40",
pages = "3543--3554",
journal = "IEEE T MED IMAGING",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

RIS

TY - JOUR

T1 - Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

AU - Campello, Victor M

AU - Gkontra, Polyxeni

AU - Izquierdo, Cristian

AU - Martin-Isla, Carlos

AU - Sojoudi, Alireza

AU - Full, Peter M

AU - Maier-Hein, Klaus

AU - Zhang, Yao

AU - He, Zhiqiang

AU - Ma, Jun

AU - Parreno, Mario

AU - Albiol, Alberto

AU - Kong, Fanwei

AU - Shadden, Shawn C

AU - Acero, Jorge Corral

AU - Sundaresan, Vaanathi

AU - Saber, Mina

AU - Elattar, Mustafa

AU - Li, Hongwei

AU - Menze, Bjoern

AU - Khader, Firas

AU - Haarburger, Christoph

AU - Scannell, Cian M

AU - Veta, Mitko

AU - Carscadden, Adam

AU - Punithakumar, Kumaradevan

AU - Liu, Xiao

AU - Tsaftaris, Sotirios A

AU - Huang, Xiaoqiong

AU - Yang, Xin

AU - Li, Lei

AU - Zhuang, Xiahai

AU - Vilades, David

AU - Descalzo, Martin L

AU - Guala, Andrea

AU - Mura, Lucia La

AU - Friedrich, Matthias G

AU - Garg, Ria

AU - Lebel, Julie

AU - Henriques, Filipe

AU - Karakas, Mahir

AU - Cavus, Ersin

AU - Petersen, Steffen E

AU - Escalera, Sergio

AU - Segui, Santi

AU - Rodriguez-Palomares, Jose F

AU - Lekadir, Karim

PY - 2021/12

Y1 - 2021/12

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.

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.

KW - Cardiac Imaging Techniques

KW - Heart/diagnostic imaging

KW - Humans

KW - Magnetic Resonance Imaging

U2 - 10.1109/TMI.2021.3090082

DO - 10.1109/TMI.2021.3090082

M3 - SCORING: Journal article

C2 - 34138702

VL - 40

SP - 3543

EP - 3554

JO - IEEE T MED IMAGING

JF - IEEE T MED IMAGING

SN - 0278-0062

IS - 12

ER -