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TANet: Towards Fully Automatic Tooth Arrangement

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12360))

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Abstract

Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics. Existing practice for specifying the target tooth arrangement involves tedious manual operations with the outcome quality depending heavily on the experience of individual specialists, leading to inefficiency and undesirable variations in treatment results. In this work, we proposed a learning-based method for fast and automatic tooth arrangement. To achieve this, we formulate the tooth arrangement task as a novel structured 6-DOF pose prediction problem and solve it by proposing a new neural network architecture to learn from a large set of clinical data that encode successful orthodontic treatment cases. Our method has been validated with extensive experiments and shows promising results both qualitatively and quantitatively.

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Author information

Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Guodong Wei & Guiqing Li

  2. The University of Hong Kong, Pok Fu Lam, Hong Kong

    Guodong Wei, Zhiming Cui, Yumeng Liu, Nenglun Chen, Runnan Chen & Wenping Wang

Authors
  1. Guodong Wei
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  2. Zhiming Cui
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  3. Yumeng Liu
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  4. Nenglun Chen
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  5. Runnan Chen
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  6. Guiqing Li
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  7. Wenping Wang
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Corresponding author

Correspondence to Wenping Wang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Wei, G. et al. (2020). TANet: Towards Fully Automatic Tooth Arrangement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12360. Springer, Cham. https://doi.org/10.1007/978-3-030-58555-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-58555-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58554-9

  • Online ISBN: 978-3-030-58555-6

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