Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of unsupervised learning with similarity metrics. Supervised training on synthetic deformations is an alternative but, in turn, suffers from the domain gap to the real domain. In this work, we aim to tackle this gap through domain adaptation. Self-training with the Mean Teacher is an established approach to this problem but is impaired by the inherent noise of the pseudo labels from the teacher. As a remedy, we present a denoised teacher-student paradigm for point cloud registration, comprising two complementary denoising strategies. First, we propose to filter pseudo labels based on the Chamfer distances of teacher and student registrations, thus preventing detrimental supervision by the teacher. Second, we make the teacher dynamically synthesize novel training pairs with noise-free labels by warping its moving inputs with the predicted deformations. Evaluation is performed for inhale-to-exhale registration of lung vessel trees on the public PVT dataset under two domain shifts. Our method surpasses the baseline Mean Teacher by 13.5/62.8%, consistently outperforms diverse competitors, and sets a new state-of-the-art accuracy (TRE=2.31mm). Code is available at https://github.com/multimodallearning/denoised_mt_pcd_reg.

翻译：基于点云的医学配准有望提高计算效率、增强对强度偏移的鲁棒性并保护匿名性，但受限于基于相似性度量的无监督学习方法的低效性。基于合成形变的监督训练是一种替代方案，但面临与真实域之间的域差异问题。本研究旨在通过域自适应解决这一差异。均值教师自训练是处理该问题的成熟方法，但受限于教师网络伪标签固有的噪声。为此，我们提出一种用于点云配准的去噪教师-学生范式，包含两种互补的去噪策略。首先，我们提出基于教师与学生配准结果的Chamfer距离过滤伪标签，从而避免教师网络的误导性监督。其次，使教师网络通过将可变形移动输入与预测形变进行融合，动态生成带有无噪声标签的新训练对。在公共PVT数据集上针对两个域偏移情景进行肺血管树吸气-呼气配准评估。本方法相较于基线均值教师网络性能提升13.5/62.8%，持续优于多种竞争方法，并创下新的最优精度（TRE=2.31mm）。代码地址：https://github.com/multimodallearning/denoised_mt_pcd_reg。