A complete digital tooth model with both the dental crown and root is of great importance for computer-aided orthodontic treatment. This paper first proposes an automatic segmentation method for complete tooth models with both the crown and reconstructed root based on multimodal data. With the laser-scanned crown mesh and cone-beam computed tomography (CBCT) data of a patient, we propose an improved iterative closest point algorithm and convex hull selection method to obtain the initial contour and slice for segmentation. Based on the initialization, we propose an improved level set method with the shape prior, named LSS, to segment the root of the tooth slice by slice. After segmentation, we reconstruct the root model and replace the crown part with the scanned crown model to solve the occlusal problem. The experiments demonstrate that our method can obtain tooth models from CBCT automatically and accurately.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
Detterbeck A, Hofmeister M, Hofmann E, Haddad D, Weber D, Hölzing A, Zabler S, Schmid M, Hiller KH, Jakob P (2016) MRI vs. ct for orthodontic applications: comparison of two MRI protocols and three ct (multislice, cone-beam, industrial) technologies. J Orofacial Orthop 77(4):1–11
Drăgan OC, Fărcăşanu AŞ, Câmpian RS, Turcu RVF (2016) Human tooth and root canal morphology reconstruction using magnetic resonance imaging. Clujul Med 89(1):137–142
Gan Y, Xia Z, Xiong J, Zhao Q, Hu Y, Zhang J (2015) Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys 42(1):14–27
Gao H, Chae O (2010) Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognit 43(7):2406–2417
Garland M, Heckbert PS (1997) Surface simplification using quadric error metrics. ACM SIGGRAPH Comput Gr 1997:209–216
Graham RL (1972) An efficient algorithm for determining the convex hull of a finite planar set. Inf Process Lett 1(4):132–133
Kuo CC, Yau HT (2005) A delaunay-based region-growing approach to surface reconstruction from unorganized points. Comput Aided Des 37(8):825–835
Landesberger TV, Basgier D, Becker M (2016) Comparative local quality assessment of 3d medical image segmentations with focus on statistical shape model-based algorithms. IEEE Trans Vis Comput Gr 22(12):2537–2549
Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 430–436. IEEE
Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243–3254
Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3d surface construction algorithm. In: ACM siggraph computer graphics, vol 21, pp 163–169. ACM
Motohashi N, Kuroda T (1999) A 3d computer-aided design system applied to diagnosis and treatment planning in orthodontics and orthognathic surgery. Eur J Orthod 21(3):263–274
Mullen SR, Martin CA, Ngan P, Gladwin M (2007) Accuracy of space analysis with emodels and plaster models. Am J Orthod Dentofac Orthop 132(3):346–352
Peck D (2008) Digital imaging and communications in medicine (DICOM). Springer, Berlin
Qian J, Sunxia Tang D, Tao Y, Lin J, Lin H (2018) A regional energy function based approach for fast tooth segmentation from cbct images. J Comput Aided Des Comput Gr 30(6):975–983
Qian J, Gao Y, Tang Y, Tao Y, Lin J, Lin H (2019) An automatic algorithm for repairing dental models based on contours. In: Tenth international conference on graphics and image processing (ICGIP 2018), vol 11069, p 1106911. SPIE
Qiu N, Fan R, You L, Jin X (2013) An efficient and collision-free hole-filling algorithm for orthodontics. Visual Comput 29(6–8):577–586
Taubin G (1995) Curve and surface smoothing without shrinkage. In: Proceedings of IEEE international conference on computer vision, pp 852–857. IEEE
Wang Y, Zhong Z, Hua J (2019) Deeporgannet: On-the-fly reconstruction and visualization of 3d/4d lung models from single-view projections by deep deformation network. IEEE Trans Vis Comput Gr 26(1):960–970
Wei X, Chen L, Gao C (2015) Automatic mesh fusion for dental crowns and roots in a computer-aided orthodontics system. In: 2015 8th international conference on biomedical engineering and informatics (BMEI), pp 280–290. IEEE
Xia G, Chen L (2014) 3d dental mesh repairing using template-based deformation. In: 2014 7th international conference on biomedical engineering and informatics, pp 410–414. IEEE
Xia Z, Gan Y, Chang L, Xiong J, Zhao Q (2017) Individual tooth segmentation from ct images scanned with contacts of maxillary and mandible teeth. Comput Methods Programs Biomed 138:1–12
Yau HT, Yang TJ, Chen YC (2014) Tooth model reconstruction based upon data fusion for orthodontic treatment simulation. Comput Biol Med 48:8–16
Zhou X, Gan Y, Xiong J, Zhang D, Zhao Q, Xia Z (2018) A method for tooth model reconstruction based on integration of multimodal images. J Healthc Eng 2018:4950131
This work was funded by the National Key R&D Program of China (Grant No. 2019YFC17902), the National Natural Science Foundation of China (Grant No. 61672452, 81827804, 61972342, 81970978), and NSFC Guangdong Joint Fund (U1611263). This research was approved by the Medical Ethics Committee of Zhejiang University School of Medicine.
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised: due to change in Electronic supplementary material.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Table 6 shows examples of reconstruction of seven types of teeth using the developed method in this paper.
About this article
Cite this article
Qian, J., Lu, S., Gao, Y. et al. An automatic tooth reconstruction method based on multimodal data. J Vis 24, 205–221 (2021). https://doi.org/10.1007/s12650-020-00697-0
- Automatic image segmentation
- Iterative closest point
- Convex hull
- Improved level set
- Shape prior
- Multimodal data
- Cone-beam computed tomography