Abstract
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.
Highlights
A CCURATE segmentation of cardiovascular magnetic resonance (CMR) images is an important pre-requisite in clinical practice to reliably diagnose and assess a number of major cardiovascular diseases [1], [2]
We present and discuss the results of the MultiVendor and Multi-Disease Cardiac Segmentation (M&Ms) challenge in detail, to which a total of 14 international teams submitted a range of solutions, including different strategies of transfer learning, domain adaptation and data augmentation, to accommodate for the differences in scanner vendors and imaging protocols
For analysing the obtained results, we implemented two baseline models to better appreciate the added value of the data augmentation and domain adaptation techniques used in this challenge: B1: A 2D UNet without any data augmentation as described in the original reference [17], trained with weighted cross entropy loss
Summary
A CCURATE segmentation of cardiovascular magnetic resonance (CMR) images is an important pre-requisite in clinical practice to reliably diagnose and assess a number of major cardiovascular diseases [1], [2]. The process typically requires the clinician to provide a significant amount of manual input and correction to accurately and consistently annotate the cardiac boundaries across all image slices and cardiac phases. In the last few years, the advent of the deep learning paradigm has motivated the development of many neural network based techniques for improved CMR segmentation, as listed in a recent review [5]. Most of these techniques have been all too often trained and evaluated using cardiac imaging samples collected from single clinical centres using similar imaging protocols. While these works have advanced the state-of-the-art in deep learning based cardiac image segmentation, their high performances were reported on samples with relatively homogeneous imaging characteristics
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