TSegNet: An efficient and accurate tooth segmentation network on 3D dental model

Zhiming Cui, Changjian Li, Nenglun Chen, Guodong Wei, Runnan Chen, Yuanfeng Zhou, Dinggang Shen, Wenping Wang

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Automatic and accurate segmentation of dental models is a fundamental task in computer-aided dentistry. Previous methods can achieve satisfactory segmentation results on normal dental models; however, they fail to robustly handle challenging clinical cases such as dental models with missing, crowding, or misaligned teeth before orthodontic treatments. In this paper, we propose a novel end-to-end learning-based method, called TSegNet, for robust and efficient tooth segmentation on 3D scanned point cloud data of dental models. Our algorithm detects all the teeth using a distance-aware tooth centroid voting scheme in the first stage, which ensures the accurate localization of tooth objects even with irregular positions on abnormal dental models. Then, a confidence-aware cascade segmentation module in the second stage is designed to segment each individual tooth and resolve ambiguities caused by aforementioned challenging cases. We evaluated our method on a large-scale real-world dataset consisting of dental models scanned before or after orthodontic treatments. Extensive evaluations, ablation studies and comparisons demonstrate that our method can generate accurate tooth labels robustly in various challenging cases and significantly outperforms state-of-the-art approaches by 6.5% of Dice Coefficient, 3.0% of F1 score in term of accuracy, while achieving 20 times speedup of computational time.
Original languageEnglish
Article number101949
Number of pages12
JournalMedical Image Analysis
Early online date19 Dec 2020
Publication statusPublished - 1 Apr 2021

Keywords / Materials (for Non-textual outputs)

  • Dental model segmentation
  • Tooth centroid prediction
  • Confidence-aware cascade segmentation
  • 3D point cloud


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