🤖 AI Summary
In digital dentistry, automatic localization of dental anatomical landmarks—such as cusp tips, facial axis points, and gingival margins—faces three key challenges: limited annotated training samples, high inter-subject anatomical variability, and the difficulty of modeling unordered 3D point clouds. To address these, we propose a geometrically aware and anatomically interpretable end-to-end point-level regression framework. Our method introduces a lightweight Point Transformer v3 module that jointly encodes point-wise features and captures local geometric structures via graph-based modeling. Furthermore, we design a graph neural network–driven non-maximum suppression (NMS) mechanism to robustly identify and refine local peaks corresponding to landmark positions. Evaluated on the MICCAI 2024 3DTeethLand Challenge, our approach achieves state-of-the-art performance, reducing mean localization error by 21.3% and significantly improving cross-subject generalization. This work establishes a reliable, clinically applicable benchmark for digital orthodontics and prosthodontics.
📝 Abstract
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.