Tooth segmentation of 3D scan data using generative adversarial networks

Taeksoo Kim, Youngmok Cho, Doojun Kim, Minho Chang, Yoon Ji Kim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


The use of intraoral scanners in the field of dentistry is increasing. In orthodontics, the process of tooth segmentation and rearrangement provides the orthodontist with insights into the possibilities and limitations of treatment. Although, full-arch scan data, acquired using intraoral scanners, have high dimensional accuracy, they have some limitations. Intraoral scanners use a stereo-vision system, which has difficulties scanning narrow interdental spaces. These areas, with a lack of accurate scan data, are called areas of occlusion. Owing to such occlusions, intraoral scanners often fail to acquire data, making the tooth segmentation process challenging. To solve the above problem, this study proposes a method of reconstructing occluded areas using a generative adversarial network (GAN). First, areas of occlusion are eliminated, and the scanned data are sectioned along the horizontal plane. Next, images are trained using the GAN. Finally, the reconstructed two-dimensional (2D) images are stacked to a three-dimensional (3D) image and merged with the data where the occlusion areas have been removed. Using this method, we obtained an average improvement of 0.004 mm in the tooth segmentation, as verified by the experimental results.

Original languageEnglish
Article number490
JournalApplied Sciences (Switzerland)
Issue number2
Publication statusPublished - 2020 Jan 1


  • Dental scan data
  • Generative adversarial networks
  • Image completion
  • Intraoral scanners
  • Occlusion areas
  • Reconstruction
  • Tooth segmentation

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


Dive into the research topics of 'Tooth segmentation of 3D scan data using generative adversarial networks'. Together they form a unique fingerprint.

Cite this