Malocclusion Treatment Planning via PointNet Based Spatial Transformation Network

Abstract

Orthodontic malocclusion treatment is a procedure to correct dental and facial morphology by moving teeth or adjusting underlying bones. It concentrates on two key aspects: the treatment planning for dentition alignment; and the plan implementation with the aid of external forces. Existing treatment planning requires significant time and effort for orthodontists and technicians. At present, no work successfully automates the process of tooth movement in orthodontics. In this study, we leverage state-of-the-art deep learning methods and propose an automated treatment planning process to take advantage of the spatial interrelationship between different teeth. Our method enables to exploit a 3-dimensional spatial transformation architecture for malocclusion treatment planning with 4 steps: (1) sub-sampling the dentition point cloud to get a critical point set; (2) extracting local features for each tooth and global features for the whole dentition; (3) obtaining transformation parameters conditioned on the features refined from the combination of both the local and global features and, (4) transforming initial dentition point cloud to the parameter-defined final state. Our approach achieves 84.5% cosine similarity accuracy (CSA) for the transformation matrix in the non-augmented dataset, and 95.3% maximum CSA for the augmented dataset. Our approach’s outcome is proven to be effective in quantitative analysis and semantically reasonable in qualitative analysis.