Abstract
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.
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Acknowledgment
This work was supported in part by United States National Institutes of Health (NIH) grants R01 DE022676, R01 DE027251, and R01 DE021863.
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Lang, Y. et al. (2022). DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_43
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DOI: https://doi.org/10.1007/978-3-031-16434-7_43
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