An automatic tooth reconstruction method based on multimodal data

A Correction to this article was published on 03 February 2021

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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.

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Change history


  1. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. Garland M, Heckbert PS (1997) Surface simplification using quadric error metrics. ACM SIGGRAPH Comput Gr 1997:209–216

    Google Scholar 

  7. Graham RL (1972) An efficient algorithm for determining the convex hull of a finite planar set. Inf Process Lett 1(4):132–133

    Article  Google Scholar 

  8. Kuo CC, Yau HT (2005) A delaunay-based region-growing approach to surface reconstruction from unorganized points. Comput Aided Des 37(8):825–835

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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

  11. 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

    MathSciNet  Article  Google Scholar 

  12. 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

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. Peck D (2008) Digital imaging and communications in medicine (DICOM). Springer, Berlin

    Google Scholar 

  16. 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

    Google Scholar 

  17. 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

  18. 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

    Article  Google Scholar 

  19. Taubin G (1995) Curve and surface smoothing without shrinkage. In: Proceedings of IEEE international conference on computer vision, pp 852–857. IEEE

  20. 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

    Google Scholar 

  21. 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

  22. 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

  23. 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

    Article  Google Scholar 

  24. Yau HT, Yang TJ, Chen YC (2014) Tooth model reconstruction based upon data fusion for orthodontic treatment simulation. Comput Biol Med 48:8–16

    Article  Google Scholar 

  25. 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

    Google Scholar 

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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.

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Correspondence to Jun Lin or Hai Lin.

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The original online version of this article was revised: due to change in Electronic supplementary material.

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Table 6 shows examples of reconstruction of seven types of teeth using the developed method in this paper.

Table 6 Results of different teeth

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Qian, J., Lu, S., Gao, Y. et al. An automatic tooth reconstruction method based on multimodal data. J Vis 24, 205–221 (2021).

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  • Automatic image segmentation
  • Iterative closest point
  • Convex hull
  • Improved level set
  • Shape prior
  • Multimodal data
  • Cone-beam computed tomography