论文标题
用于域适应的直方图匹配增强，并应用于多中心，多供应商和多疾病心脏图像分割
Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation
论文作者
论文摘要
如果训练案例和测试案例来自相同的分布，则卷积神经网络（CNN）的心脏结构分割非常准确。但是，如果测试案例来自不同的域（例如，新的MRI扫描仪，临床中心），则性能将降低。在本文中，我们提出了一种直方图匹配（HM）数据增强方法来消除域间隙。具体而言，我们的方法通过使用HM将测试案例的强度分布转移到现有培训案例中来生成新的培训案例。提出的方法非常简单，可以在许多分割任务中以插件的方式使用。该方法在MICCAI 2020 M \＆MS挑战中进行评估，并分别为左心室，心肌和右杂志，达到了0.9051、0.8405和0.8749的平均骰子得分，Hausdorff距离为9.996、12.49和12.68。我们的结果排在Miccai 2020 M \＆MS挑战中的第三名。代码和训练有素的模型可在\ url {https://github.com/junma11/hm_dataaug}上公开获得。
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}.
