Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.

Journal: IEEE transactions on medical imaging
Published Date:

Abstract

Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooth's region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.964±0.054 , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of 0.597±0.761 mm in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.

Authors

  • Tai-Hsien Wu
  • Chunfeng Lian
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. Electronic address: chunfeng_lian@med.unc.edu.
  • Sanghee Lee
    Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA.
  • Matthew Pastewait
    United States Air Force, Kadena AB, Kadena, Japan.
  • Christian Piers
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Fan Wang
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Chiung-Ying Chiu
  • Wenchi Wang
  • Christina Jackson
  • Wei-Lun Chao
    Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, USA.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Ching-Chang Ko