用Transformer预测牙齿排列，兼顾咬合与碰撞约束。
Transformer-Based Tooth Alignment Prediction With Occlusion And Collision Constraints
- 基于Swin-Transformer构建轻量网络，处理3D点云更高效
- 新设计咬合损失函数，实现上下颌咬合关系定量评估
- 自建591例数据集，支持复杂病例训练与社区共享
数字化正畸治疗规划需精准预测牙齿排列，传统人工方式耗时费力且依赖临床经验。本文提出一种基于Swin-Transformer的轻量级牙齿排列神经网络。首先，基于虚拟牙弓线重新组织3D点云，并转换为有序多通道纹理，提升精度与效率。其次，设计两种新型咬合损失函数，首次在该领域量化评估上下颌咬合关系，作为重要临床约束，显著提升预测准确率。为训练模型，收集包含591个临床病例的大规模数字正畸数据集，涵盖多种复杂情况，目前尚无公开数据集，本数据集发布后将惠及学术界。此外，提出两种考虑牙齿空间分布与咬合关系的数据增强方法。通过大量实验验证，包括与主流方法对比及消融研究，证明本方法具备高预测精度。
原文摘要 · Abstract (English)
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.
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