Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 40, Issue 12, Pages 3543-3554Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2021.3090082
Keywords
Image segmentation; Heart; Training; Hospitals; Deep learning; Biomedical engineering; Protocols; Cardiovascular magnetic resonance; image segmentation; deep learning; generalizability; data augmentation; domain adaption; public dataset
Categories
Funding
- European Union [825903]
- Engineering and Physical Sciences Research Council [EP/P022928/1] Funding Source: researchfish
Ask authors/readers for more resources
The emergence of deep learning has advanced cardiac magnetic resonance segmentation, yet current models lack generalizability. A recent competition emphasized the importance of data augmentation in training deep learning models and provided new data resources for future research.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available