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Tooth segmentation and gingival tissue deformation framework for 3D orthodontic treatment planning and evaluating

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Abstract

In this study, we propose an integrated tooth segmentation and gingival tissue deformation simulation framework used to design and evaluate the orthodontic treatment plan especially with invisible aligners. Firstly, the bio-characteristics information of the digital impression is analyzed quantitatively and demonstrated visually. With the derived information, the transitional regions of tooth-tooth and tooth-gingiva are extracted as the solution domain of the segmentation boundaries. Then, a boundary detection approach is proposed, which is used for the tooth segmentation and region division of the digital impression. After tooth segmentation, we propose the deformation simulation framework driven by energy function based on the biological deformation properties of gingival tissues. The correctness and availability of the proposed segmentation and gingival tissue deformation simulation framework are demonstrated with typical cases and qualitative analysis. Experimental results show that segmentation boundaries calculated by the proposed method are accurate, and local details of the digital impression under study are preserved well during deformation simulation. Qualitative analysis results of the gingival tissues’ surface area and volume variations indicate that the proposed gingival tissue deformation simulation framework is consistent with the clinical gingival tissue deformation characteristics, and it can be used to predict the rationality of the treatment plan from both visual inspection and numerical simulation. The proposed tooth segmentation and gingival tissue deformation simulation framework is shown to be effective and has good practicability, but accurate quantitative analysis based on clinical results is still an open problem in this study. Combined with tooth rearrangement steps, it can be used to design the orthodontic treatment plan, and to output the data for production of invisible aligners.

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Funding

This work was supported by the National Natural Science Foundation of China (grant number 51705183).

Author information

Authors and Affiliations

  1. Huaiyin Institute of Technology, Huai’an, China

    Tianran Yuan, Yimin Wang & Zhiwei Hou

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Tianran Yuan & Jun Wang

Authors
  1. Tianran Yuan
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  2. Yimin Wang
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  3. Zhiwei Hou
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  4. Jun Wang
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Correspondence to Tianran Yuan.

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Highlights

• The digital impression can be segmented effectively and accurately.

• Local details of the digital impression can be preserved well during the process of deformation simulation.

• Qualitative analysis results of the volume and surface area variations indicate that the proposed gingival tissue deformation simulation framework can be used to simulate the gingival tissue deformation behavior.

• The proposed simulation framework can be used to design an orthodontic treatment plan and predict its rationality from both visual inspection and numerical simulation.

• The output data corresponding to the intermediate step’s digital impression can be used to make a mother mold for the production of invisible aligners.

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Appendix

Appendix

Fig. 14
figure 14

Orthodontic treatment plan for a typical case with severe malocclusion, the treatment process is divided into 60steps and wearing 1 week for one step. a Initial position state with attachments. b Final target position state with attachments. c Intermediate steps(0,16,24,35,44,60) of the upper jaw and its corresponding 3D printed mother mold. d Intermediate steps (0,16,24,35,44,60) of the lower jaw and its corresponding 3D printed mother mold. e Invisible aligner corresponding to the 60 steps of the upper jaw in c

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Fig. 15
figure 15

Clinical treatment results after wearing about 60 steps of invisible aligners by using orthodontic treatment plan in Fig. 14

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Fig. 16
figure 16

Orthodontic treatment plan analysis with initial and final target position state. a Plaster model. b Digital impression. c Teeth needed to be realigned with green color. d Target position of the green teeth in c. e Deformation amount analysis between the initial state in c and the final state in d

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Yuan, T., Wang, Y., Hou, Z. et al. Tooth segmentation and gingival tissue deformation framework for 3D orthodontic treatment planning and evaluating. Med Biol Eng Comput 58, 2271–2290 (2020). https://doi.org/10.1007/s11517-020-02230-9

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  • DOI: https://doi.org/10.1007/s11517-020-02230-9

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