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

By Víctor M. Campello, P. Gkontra, C. Izquierdo, Carlos Martín-Isla, A. Sojoudi, Peter M. Full, Klaus Hermann Maier-Hein, Yao Zhang, Zhiqiang He, Jun Ma and 36 more. IEEE Transactions on Medical Imaging, 2021

467 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/edfc14cc234122533a4f3728a36e54dfd6de6211

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Citations in the corpus

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “However, a clinical applicable deep learning model should be generalized suitably across multiple centres and scanner vendors from different domains [123].”
Segment anything in medical images (2023, unknown): “In cardiac imaging, segmentation of structures such as the left ventricle in echocardiograms or MRIs is essential for assessing cardiac function and diagnosing conditions like heart failure [13].”
Transformers in Medical Image Analysis: A Review (2022, unknown): “[74] cardiac segmentation M&Ms [75] 88.”
Segment Anything in Medical Images (2023, background): “However, its overall performance still lags behind specialist models, such as those developed for liver segmentation in abdomen CT images [33] and left and right ventricle segmentation in heart MR images [10], particularly in terms of boundary consensus.”
Transformers in Medical Imaging: A Survey (2022, background): “Experiments show strong generalization ability of UTNet on multi-label and multi-vendor cardiac MRI challenge dataset cohort [175].”
Federated learning for medical image analysis: A survey (2023, background): “6) M & M: Multi-Center, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M & Ms) Challenge [112] 13 is a publicly available cardiac MRI dataset.”