Recently, anatomical landmark detection has achieved great progresses on single-domain data, which usually assumes training and test sets are from the same domain. However, such an assumption is not always true in practice, which can cause significant performance drop due to domain shift. To tackle this problem, we propose a novel framework for anatomical landmark detection under the setting of unsupervised domain adaptation (UDA), which aims to transfer the knowledge from labeled source domain to unlabeled target domain. The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation. Specifically, a self-training strategy is proposed to select reliable landmark-level pseudo-labels of target domain data with dynamic thresholds, which makes the adaptation more effective. Furthermore, a domain adversarial learning module is designed to handle the unaligned data distributions of two domains by learning domain-invariant features via adversarial training. Our experiments on cephalometric and lung landmark detection show the effectiveness of the method, which reduces the domain gap by a large margin and outperforms other UDA methods consistently. The code is available at https://github.com/jhb86253817/UDA_Med_Landmark.

翻译：近来，解剖标志点检测在单域数据上取得了重大进展，这类方法通常假设训练集和测试集来自同一域。然而，该假设在实践中并不总是成立，域偏移可能导致性能显著下降。为解决该问题，我们提出了一种新颖的无监督域适应（UDA）框架用于解剖标志点检测，旨在将知识从带标签的源域迁移至无标签的目标域。该框架利用自训练和域对抗学习来弥合适应过程中的域差异。具体而言，我们提出了一种自训练策略，通过动态阈值从目标域数据中选取可靠的标志级伪标签，从而提升适应效果。此外，我们设计了域对抗学习模块，通过对抗训练学习域不变特征，以应对两域间未对齐的数据分布问题。在头影测量和肺部标志点检测上的实验表明，该方法有效大幅缩小了域差异，且一致优于其他无监督域适应方法。代码已开源至 https://github.com/jhb86253817/UDA_Med_Landmark。