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 this https URL

我们提出了一种基于无监督领域自适应的解剖标志点检测新框架，利用自我训练和领域对抗学习来处理适应过程中的领域差异，通过选择可靠的动态阈值标签进行目标领域数据的自适应，以及通过对抗训练学习领域不变特征来处理两个领域的不对齐数据分布，实验证明该方法在头影测量和肺标志点检测方面有效，显著减小了领域差距并且持续优于其他无监督领域自适应方法。

无监督领域适应在解剖标志检测中的应用

Unsupervised Domain Adaptation for Anatomical Landmark Detection

Machine learning-based approaches outperform competing methods in most
disciplines relevant to diagnostic radiology. Interventional radiology,
however, has not yet benefited substantially from the advent of deep learning,
in particular because of two reasons: 1) Most images acquired during the
procedure are never archived and are thus not available for learning, and 2)
even if they were available, annotations would be a severe challenge due to the
vast amounts of data. When considering fluoroscopy-guided procedures, an
interesting alternative to true interventional fluoroscopy is in silico
simulation of the procedure from 3D diagnostic CT. In this case, labeling is
comparably easy and potentially readily available, yet, the appropriateness of
resulting synthetic data is dependent on the forward model. In this work, we
propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy
and digital radiography from CT scans, tightly integrated with the software
platforms native to deep learning. We use machine learning for material
decomposition and scatter estimation in 3D and 2D, respectively, combined with
analytic forward projection and noise injection to achieve the required
performance. On the example of anatomical landmark detection in X-ray images of
the pelvis, we demonstrate that machine learning models trained on DeepDRRs
generalize to unseen clinically acquired data without the need for re-training
or domain adaptation. Our results are promising and promote the establishment
of machine learning in fluoroscopy-guided procedures.

本研究提出了一种基于机器学习的用于快速、真实模拟 CT 扫描的衍射、数字放射成像模拟的框架，并通过在盆骨的影像检测中的应用展示了模型无需重新训练和领域适应即可推广到未知的临床数据的良好表现。