The planning of digital orthodontic treatment requires providing tooth alignment, which not only consumes a lot of time and labor to determine manually but also relays clinical experiences heavily. In this work, we proposed a lightweight tooth alignment neural network based on Swin-transformer. We first re-organized 3D point clouds based on virtual arch lines and converted them into order-sorted multi-channel textures, which improves the accuracy and efficiency simultaneously. We then designed two new occlusal loss functions that quantitatively evaluate the occlusal relationship between the upper and lower jaws. They are important clinical constraints, first introduced to the best of our knowledge, and lead to cutting-edge prediction accuracy. To train our network, we collected a large digital orthodontic dataset that has 591 clinical cases, including various complex clinical cases. This dataset will benefit the community after its release since there is no open dataset so far. Furthermore, we also proposed two new orthodontic dataset augmentation methods considering tooth spatial distribution and occlusion. We evaluated our method with this dataset and extensive experiments, including comparisons with STAT methods and ablation studies, and demonstrate the high prediction accuracy of our method.

翻译：数字化正畸治疗规划需要提供牙齿排列方案，这不仅耗费大量时间和人力进行手动确定，而且高度依赖临床经验。本研究提出了一种基于Swin-transformer的轻量级牙齿排列神经网络。我们首先基于虚拟牙弓线重组三维点云数据，并将其转换为有序排序的多通道纹理，从而同步提升了预测精度与效率。随后设计了两种新型咬合损失函数，用于定量评估上下颌间的咬合关系。据我们所知，这些重要的临床约束条件首次被引入模型，并实现了前沿的预测精度。为训练网络，我们收集了包含591例临床病例的大型数字化正畸数据集，涵盖各类复杂临床情况。该数据集发布后将惠及研究社区，因为目前尚无公开数据集可用。此外，我们还提出了两种考虑牙齿空间分布与咬合关系的新型正畸数据增强方法。通过该数据集及大量实验（包括与STAT方法的对比及消融研究）评估了本方法，结果证明了其高预测精度。