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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
文献类型：期刊论文
| 作者 | Campello, Victor M.36; Gkontra, Polyxeni36; Izquierdo, Cristian36; Martin-Isla, Carlos36; Sojoudi, Alireza35; Full, Peter M.34; Maier-Hein, Klaus34; Zhang, Yao33; He, Zhiqiang32; Ma, Jun31 |
| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 40期号:12页码:3543-3554 |
| ISSN号 | 0278-0062 |
| 关键词 | Image segmentation Heart Training Hospitals Deep learning Biomedical engineering Protocols Cardiovascular magnetic resonance image segmentation deep learning generalizability data augmentation domain adaption public dataset |
| DOI | 10.1109/TMI.2021.3090082 |
| 英文摘要 | 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. |
| 资助项目 | European Union[825903] |
| WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000724511900026 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18062]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Campello, Victor M. |
| 作者单位 | 1.Univ Autonoma Barcelona, Comp Vis Ctr, Barcelona 08193, Spain 2.Univ Barcelona, Dept Matemat & Informat, Barcelona 08007, Spain 3.Queen Mary Univ London, NIHR Barts Biomed Res Ctr, William Harvey Res Inst, London E1 4NS, England 4.Barts Hlth NHS Trust, Barts Heart Ctr, London E1 1BB, England 5.German Ctr Cardiovasc Res DZHK, D-10785 Berlin, Germany 6.Univ Heart & Vasc Ctr Hamburg, Dept Cardiol, D-20251 Hamburg, Germany 7.McGill Univ, Dept Med & Diagnost Radiol, Montreal, PQ H3A 0G4, Canada 8.Univ Naples Federico II, Dept Adv Biomed Sci, I-80138 Naples, Italy 9.Univ Autonoma Barcelona, Hosp Univ Vall dHebron, Vall dHebron Inst Recerca, CIBERCV,Dept Cardiol, Barcelona 08193, Spain 10.Univ Autonoma Barcelona, Cardiol Serv, Cardiac Imaging Unit, Hosp Santa Creu & St Pau, Barcelona, Spain |
| 推荐引用方式 GB/T 7714 | Campello, Victor M.,Gkontra, Polyxeni,Izquierdo, Cristian,et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2021,40(12):3543-3554. |
| APA | Campello, Victor M..,Gkontra, Polyxeni.,Izquierdo, Cristian.,Martin-Isla, Carlos.,Sojoudi, Alireza.,...&Lekadir, Karim.(2021).Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.IEEE TRANSACTIONS ON MEDICAL IMAGING,40(12),3543-3554. |
| MLA | Campello, Victor M.,et al."Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge".IEEE TRANSACTIONS ON MEDICAL IMAGING 40.12(2021):3543-3554. |
入库方式： OAI收割
来源：计算技术研究所
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