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

Yankun Lang, Xiaoyang Chen, Hannah H. Deng, Tianshu Kuang, Joshua C. Barber, Jaime Gateno, Pew Thian Yap, James J. Xia

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783031164330
StatePublished - 2022
Event25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 18 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13432 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022


  • 3D dental surface
  • Landmark localization
  • Region proposal generation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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