Domain adaptation has great values in unpaired cross-modality image segmentation, where the training images with gold standard segmentation are not available from the target image domain. The aim is to reduce the distribution discrepancy between the source and target domains. Hence, an effective measurement for this discrepancy is critical. In this work, we propose a new metric based on characteristic functions of distributions. This metric, referred to as CF distance, enables explicit domain adaptation, in contrast to the implicit manners minimizing domain discrepancy via adversarial training. Based on this CF distance, we propose an unsupervised domain adaptation framework for cross-modality cardiac segmentation, which consists of image reconstruction and prior distribution matching. We validated the method on two tasks, i.e., the CT-MR cross-modality segmentation and the multi-sequence cardiac MR segmentation. Results showed that the proposed explicit metric was effective in domain adaptation, and the segmentation method delivered promising and superior performance, compared to other state-of-the-art techniques. The data and source code of this work has been released via https://zmiclab.github.io/projects.html .

中文翻译：

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CF距离：一种新的域差异度量标准并将其应用于跨模态心脏图像分割的显式域自适应。

域匹配在不成对的跨模态图像分割中具有重要的价值，在这种情况下，从目标图像域中无法获得具有黄金标准分割的训练图像。目的是减少源域和目标域之间的分布差异。因此，有效测量此差异至关重要。在这项工作中，我们提出了一种基于分布特征函数的新度量。该指标称为CF距离，可以实现显式的 域适应，与 隐含的通过对抗训练最小化领域差异的方式。基于此CF距离，我们提出了一种用于跨模态心脏分割的无监督域自适应框架，该框架包括图像重建和先验分布匹配。我们在两个任务上验证了该方法，即CT-MR交叉模态分割和多序列心脏MR分割。结果表明，与其他最新技术相比，拟议的显式度量在域自适应方面有效，并且分割方法提供了有希望的优越性能。该作品的数据和源代码已通过以下方式发布：https://zmiclab.github.io/projects.html 。