  Convolutional Neural Networks (CNNs) have achieved high accuracy for cardiac
structure segmentation if training cases and testing cases are from the same
distribution. However, the performance would be degraded if the testing cases
are from a distinct domain (e.g., new MRI scanners, clinical centers). In this
paper, we propose a histogram matching (HM) data augmentation method to
eliminate the domain gap. Specifically, our method generates new training cases
by using HM to transfer the intensity distribution of testing cases to existing
training cases. The proposed method is quite simple and can be used in a
plug-and-play way in many segmentation tasks. The method is evaluated on MICCAI
2020 M\&Ms challenge, and achieves average Dice scores of 0.9051, 0.8405, and
0.8749, and Hausdorff Distances of 9.996, 12.49, and 12.68 for the left
ventricular, myocardium, and right ventricular, respectively. Our results rank
the third place in MICCAI 2020 M\&Ms challenge. The code and trained models are
publicly available at \url{https://github.com/JunMa11/HM_DataAug}.
