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

doi:10.1007/978-3-031-16434-7_43

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 proceeding › Conference contribution

2 Scopus citations

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.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	444-452
Number of pages	9
ISBN (Print)	9783031164330
DOIs	https://doi.org/10.1007/978-3-031-16434-7_43
State	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: Sep 18 2022 → Sep 22 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13432 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	9/18/22 → 9/22/22

Keywords

3D dental surface
Landmark localization
Region proposal generation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-031-16434-7_43

Cite this

Lang, Y., Chen, X., Deng, H. H., Kuang, T., Barber, J. C., Gateno, J., Yap, P. T., & Xia, J. J. (2022). DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (pp. 444-452). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13432 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16434-7_43

DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models. / Lang, Yankun; Chen, Xiaoyang; Deng, Hannah H. et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. p. 444-452 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13432 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lang, Y, Chen, X, Deng, HH, Kuang, T, Barber, JC, Gateno, J, Yap, PT & Xia, JJ 2022, DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13432 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 444-452, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 9/18/22. https://doi.org/10.1007/978-3-031-16434-7_43

Lang Y, Chen X, Deng HH, Kuang T, Barber JC, Gateno J et al. DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 444-452. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-16434-7_43

Lang, Yankun ; Chen, Xiaoyang ; Deng, Hannah H. et al. / DentalPointNet : Landmark Localization on High-Resolution 3D Digital Dental Models. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer Science and Business Media Deutschland GmbH, 2022. pp. 444-452 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4391a8d6bc6e4cdf9f10689613b9a667,

title = "DentalPointNet: Landmark Localization on High-Resolution 3D Digital Dental Models",

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.",

keywords = "3D dental surface, Landmark localization, Region proposal generation",

author = "Yankun Lang and Xiaoyang Chen and Deng, {Hannah H.} and Tianshu Kuang and Barber, {Joshua C.} and Jaime Gateno and Yap, {Pew Thian} and Xia, {James J.}",

note = "Funding Information: Acknowledgment. This work was supported in part by United States National Institutes of Health (NIH) grants R01 DE022676, R01 DE027251, and R01 DE021863. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",

year = "2022",

doi = "10.1007/978-3-031-16434-7_43",

language = "English (US)",

isbn = "9783031164330",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "444--452",

editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",

address = "Germany",

}

TY - GEN

T1 - DentalPointNet

T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

AU - Lang, Yankun

AU - Chen, Xiaoyang

AU - Deng, Hannah H.

AU - Kuang, Tianshu

AU - Barber, Joshua C.

AU - Gateno, Jaime

AU - Yap, Pew Thian

AU - Xia, James J.

N1 - Funding Information: Acknowledgment. This work was supported in part by United States National Institutes of Health (NIH) grants R01 DE022676, R01 DE027251, and R01 DE021863. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - 3D dental surface

KW - Landmark localization

KW - Region proposal generation

UR - http://www.scopus.com/inward/record.url?scp=85139030680&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85139030680&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-16434-7_43

DO - 10.1007/978-3-031-16434-7_43

M3 - Conference contribution

AN - SCOPUS:85139030680

SN - 9783031164330

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 444

EP - 452

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings

A2 - Wang, Linwei

A2 - Dou, Qi

A2 - Fletcher, P. Thomas

A2 - Speidel, Stefanie

A2 - Li, Shuo

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 18 September 2022 through 22 September 2022

ER -