Fully Automated Tooth Segmentation and Labeling for Both Full- and Partial-Arch Intraoral Scans Using Deep Learning

• Published in: International dental journal Vol. 75; no. 5; p. 100950
• Main Authors: Cao, Lingyun, van Nistelrooij, Niels, Liu, Jiaqi, Vinayahalingam, Shankeeth, Cenci, Maximiliano Sergio, Xi, Tong, Loomans, Bas A.C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.10.2025; Elsevier
• Subjects: Algorithms; Artificial intelligence; Correlation analysis; Deep Learning; Dentistry; FDI labeling; Full-arch intraoral scan; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Partial-arch intraoral scan; Scientific Research Report; Tooth - diagnostic imaging; Tooth segmentation; Full-arch intraoral scan; FDI labeling; Tooth segmentation; Partial-arch intraoral scan; Correlation analysis; Artificial intelligence
• ISSN: 0020-6539; 1875-595X; 1875-595X
• DOI: 10.1016/j.identj.2025.100950

Partial-arch intraoral scans (IOSs) are commonly used in clinical dentistry where high precision and reduced scanning areas are required. However, most existing tooth segmentation algorithms are developed only for full-arch IOSs and perform poorly when applied to partial-arch data. This study aimed to develop a fully automated deep learning (DL) model for tooth segmentation and labeling on both full- and partial-arch IOSs. We collected 600 IOSs (300 full-arch and 300 partial-arch) from a dental clinic. The proposed model was based on a two-stage DL model (ToothInstanceNet), and incorporated four enhancements: (1) artificial partial-arch IOSs, (2) DL-based alignment module, (3) FDI-aware postprocessing algorithm, and (4) real partial-arch IOSs. Model performance was evaluated via 5-fold cross-validation using F1-score, tooth Dice, tooth macro-F1, and macro-IoU. In addition, the model was evaluated with the public Teeth3DS dataset, and we analysed correlations between dental conditions and model errors. The model achieved an F1-score of 0.9908 and 0.9884; tooth Dice of 0.9819 and 0.9862; tooth macro-F1 of 0.9940 and 0.9786; and macro-IoU of 0.9403 and 0.9280 on full- and partial-arch IOSs, respectively. The model also demonstrated superior performance (score = 0.9870) in 3DTeethSeg challenge. Correlation analyses revealed that certain dental conditions, particularly residual roots, residual crowns, missing teeth, and partially erupted teeth, were significantly and positively associated with the model’s errors. The current study proposes the first fully automated method for tooth segmentation and FDI labeling on both full- and partial-arch IOSs. The final model demonstrated high accuracy for both scan types, indicating its potential for integration into clinical dental work. This work could aid clinicians in the first step of tooth identification in digital dental workflows, and lays the groundwork for extending the automation of the downstream applications, such as diagnosis and monitoring on partial-arch IOSs.