Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
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多中心，多供应商和多疾病心脏分割：M&Ms的挑战
DOI：
10.1109/tmi.2021.3090082
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发表时间：
2021-12-01
影响因子：
10.6
通讯作者：
Lekadir, Karim
中科院分区：
文献类型：
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作者：
Campello, Victor M.;Gkontra, Polyxeni;Lekadir, Karim
深度学习的出现大大推进了心脏磁共振（CMR）分割的最新技术。在过去的几年里，已经提出了许多技术，使自动分割的准确性接近人类的表现。然而，这些模型经常使用来自单个临床中心或同质成像协议的心脏成像样本进行训练和验证。这阻碍了在不同临床中心、成像条件或扫描仪供应商之间可推广的模型的开发和验证。为了促进心脏分割的可推广深度学习领域的进一步研究和科学基准，本文介绍了最近作为MICCAI 2020会议的一部分组织的多中心，多供应商和多疾病心脏分割（M&MS）挑战赛的结果。共有14个团队提交了不同的解决方案，结合了各种基线模型，数据增强策略和域适应技术。所获得的结果表明强度驱动的数据增强的重要性，以及需要进一步的研究，以提高对看不见的扫描仪供应商或新的成像协议的推广。此外，我们提出了一个新的资源，375异构CMR数据集，通过使用四个不同的扫描仪供应商在六家医院和三个不同的国家（西班牙，加拿大和德国），我们提供的社区开放获取，使未来的研究领域。
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.