Oral-3D: Reconstructing the 3D Structure of Oral Cavity from Panoramic X-ray

Authors

  • Weinan Song University of California, Los Angeles
  • Yuan Liang University of California, Los Angeles
  • Jiawei Yang University of California, Los Angeles
  • Kun Wang University of California, Los Angeles
  • Lei He University of California, Los Angeles

Keywords:

Healthcare, Medicine & Wellness, 3D Computer Vision

Abstract

Panoramic X-ray (PX) provides a 2D picture of the patient's mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch. Specifically, we first train a generative model to learn the cross-dimension transformation from 2D to 3D. Then we restore the shape of the oral cavity with a deformation module with the dental arch curve, which can be obtained simply by taking a photo of the patient's mouth. To be noted, Oral-3D can restore both the density of bony tissues and the curved mandible surface. Experimental results show that Oral-3D can efficiently and effectively reconstruct the 3D oral structure and show critical information in clinical applications, e.g., tooth pulling and dental implants. To the best of our knowledge, we are the first to explore this domain transformation problem between these two imaging methods.

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Published

2021-05-18

How to Cite

Song, W., Liang, Y., Yang, J., Wang, K., & He, L. (2021). Oral-3D: Reconstructing the 3D Structure of Oral Cavity from Panoramic X-ray. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 566-573. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16135

Issue

Section

AAAI Technical Track on Application Domains