DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models

Dental landmark localization is an essential step for analyzing dental models in orthodontic treatment planning and orthognathic surgery. Typically, more than 60 landmarks need to be manually digitized on a 3D dental surface model. However, most existing landmark localization methods are unable to perform reliably especially for partially edentulous patients with missing landmarks. In this work, we propose a deep learning framework, DentalPointNet, to automatically locate 68 landmarks on high-resolution dental surface models. Landmark area proposals are first predicted by a curvature-constrained region proposal network. Each proposal is then refined for landmark localization using a bounding box refinement network. Evaluation using 77 real-patient high-resolution dental surface models indicates that our approach achieves an average localization error of 0.24 mm, a false positive rate of 1% and a false negative rate of 2% on subjects both with or without partial edentulous, significantly outperforming relevant start-of-the-art methods.