mlsanityData to 14 Oct 2026

Construction of unbiased dental template and parametric dental model for precision digital dentistry

arXiv 2304.03556 ↗First posted 7 Apr 2023cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Dental template and parametric dental models are important tools for various applications in digital dentistry. However, constructing an unbiased dental template and accurate parametric dental models remains a challenging task due to the complex anatomical and morphological dental structures and also low volume ratio of the teeth. In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation. First, to address the challenges, we propose to enhance the CBCT images and their segmentation images, including image cropping, image masking and segmentation intensity reassigning. Then, we further use the segmentation images to perform co-registration with the CBCT images to generate an accurate dental atlas, from which an unbiased dental template can be generated. By leveraging the unbiased dental template, we construct parametric dental models by estimating point-to-point correspondences between the dental models and employing Principal Component Analysis to determine shape subspaces of the parametric dental models. A total of 159 CBCT images of real subjects are collected to perform the constructions. Experimental results demonstrate effectiveness of our proposed method in constructing unbiased dental template and parametric dental model. The developed dental template and parametric dental models are available at https://github.com/Marvin0724/Teeth_template.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 1 of its 34 references that are in the map. First posted on arXiv 7 Apr 2023.

Builds on

1 of 34 references.

References · 1

  1. 2022A multi-patient analysis of the center of rotation trajectories using finite element models of the human mandiblecited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.