用点云变压器自动识别牙科解剖标志点，提升正畸设计效率。
Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry
- 基于点云的Transformer架构捕捉牙齿几何特征
- 在3DTeethLand挑战中实现高精度地标定位
- 适合牙科数字化与智能正畸系统研发者
口内扫描设备的普及提升了其在现代正畸临床中的重要性。医生需借助计算机辅助设计技术，手动标记关键解剖点，如牙尖、近远中位置、颊轴点及牙龈边界，过程耗时且易出错。本文基于MICCAI 2024年3DTeethLand竞赛开展实验，提出一种受Point Transformer v3启发的模块，用于捕捉点云数据中的几何与解剖特征，并通过轻量解码器预测每个点的距离，再经图结构非极大值抑制优化输出。结果表明该方法在小样本、高变异性的口腔点云上表现优异，且所学特征具备可解释性。
原文摘要 · Abstract (English)
The increasing availability of intraoral scanning devices has heightened their importance in modern clinical orthodontics. Clinicians utilize advanced Computer-Aided Design techniques to create patient-specific treatment plans that include laboriously identifying crucial landmarks such as cusps, mesial-distal locations, facial axis points, and tooth-gingiva boundaries. Detecting such landmarks automatically presents challenges, including limited dataset sizes, significant anatomical variability among subjects, and the geometric nature of the data. We present our experiments from the 3DTeethLand Grand Challenge at MICCAI 2024. Our method leverages recent advancements in point cloud learning through transformer architectures. We designed a Point Transformer v3 inspired module to capture meaningful geometric and anatomical features, which are processed by a lightweight decoder to predict per-point distances, further processed by graph-based non-minima suppression. We report promising results and discuss insights on learned feature interpretability.
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