Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
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...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/184038 |
| Acceso en línea: | https://hdl.handle.net/2445/184038 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge automàtic Imatges per ressonància magnètica Processament digital d'imatges Machine learning Magnetic resonance imaging Digital image processing |
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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge Campello, Víctor Manuel Gkontra, Polyxeni Izquierdo, Cristián 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- Aprenentatge automàtic Imatges per ressonància magnètica Processament digital d'imatges Machine learning Magnetic resonance imaging Digital image processing 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. Institute of Electrical and Electronics Engineers (IEEE) https://hdl.handle.net/2445/184038 |
| title |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
| spellingShingle |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge Campello, Víctor Manuel Aprenentatge automàtic Imatges per ressonància magnètica Processament digital d'imatges Machine learning Magnetic resonance imaging Digital image processing |
| title_short |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
| title_full |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
| title_fullStr |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
| title_full_unstemmed |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
| title_sort |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge |
| author |
Campello, Víctor Manuel |
| author_facet |
Campello, Víctor Manuel Gkontra, Polyxeni Izquierdo, Cristián 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- |
| author_role |
author |
| author2 |
Gkontra, Polyxeni Izquierdo, Cristián 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- |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| topic |
Aprenentatge automàtic Imatges per ressonància magnètica Processament digital d'imatges Machine learning Magnetic resonance imaging Digital image processing |
| topic_facet |
Aprenentatge automàtic Imatges per ressonància magnètica Processament digital d'imatges Machine learning Magnetic resonance imaging Digital image processing |
| description |
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. |
| publishDate |
2021 |
| format |
article |
| status_str |
publishedVersion |
| url |
https://hdl.handle.net/2445/184038 |
| eu_rights_str_mv |
openAccess |
| publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
| institution |
Universidad de Barcelona |
| collection |
Dipòsit Digital de la UB |
| reponame_str |
Dipòsit Digital de la UB |
| instname_str |
Universidad de Barcelona |
| _version_ |
1878731355318124544 |
| publishDateSort |
2021 |
| author_browse |
Acero Corral, Jorge Albiol, Alberto Campello, Víctor Manuel Carscadden, Adam Descalzo, Martín L. Elattar, Mustafa Escalera Guerrero, Sergio Friedrich, Matthias G. Full, Peter M. Gkontra, Polyxeni Guala, Andrea Haarburger, Christoph He, Zhiqiang Huang, Xiaoqiong Izquierdo, Cristián Khader, Firas Kong, Fanwei La Mura, Lucía Lekadir, Karim, 1977- Li, Hongwei Li, Lei Liu, Xiao Ma, Jun Maier-Hein, Klaus Martin-Isla, Carlos Menze, Bjoern Parreño, Mario Punithakumar, Kumaradevan Saber, Mina Scannell, Cian M. Seguí Mesquida, Santi Shadden, Shawn C. Sojoudi, Alireza Sundaresan, Vaanathi Tsaftaris, Sotirios A. Veta, Mitko Viladés, David Yang, Xin Zhang, Yao Zhuang, Xiahai |
| publisherStr |
Institute of Electrical and Electronics Engineers (IEEE) |
| score |
6,8972664 |