Intra-oral scan segmentation using deep learning
Name
12903_2023_Article_3362.pdf
Size
1.91 MB
Format
Adobe PDF
Checksum (MD5)
e91a1df188ab8c76f1d8fcb0bc4b3225
Author(s)
Vinayahalingam, Shankeeth
Kempers, Steven
Schoep, Julian
Hsu, Tzu-Ming H.
Moin, David A.
van Ginneken, Bram
Flügge, Tabea
Hanisch, Marcel
Xi, Tong
Date Issued
September 5, 2023
Publisher
BioMed Central
Citation
BMC Oral Health. 2023 Sep 05;23(1):643
Version
Final published version
Abstract
Abstract
Objective
Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning.
Material and methods
As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions.
Results
The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges.
Conclusion
The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans.
Clinical significance
Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s12903-023-03362-8
