Study on dental image segmentation and automatic root canal measurement based on multi-stage deep learning using cone beam computed tomography.
10.7507/1001-5515.202503008
- Author:
Ziqing CHEN
1
;
Qi LIU
1
;
Jialei WANG
2
;
Nuo JI
1
;
Yuhang GONG
1
;
Bo GAO
2
Author Information
1. School of Biomedical Engineering, Sichuan University, Chengdu 610065, P. R. China.
2. National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, P. R. China.
- Publication Type:Journal Article
- Keywords:
Cone beam computed tomography;
Deep learning;
Root canal measurement;
Tooth instance segmentation
- MeSH:
Cone-Beam Computed Tomography/methods*;
Deep Learning;
Humans;
Dental Pulp Cavity/diagnostic imaging*;
Image Processing, Computer-Assisted/methods*
- From:
Journal of Biomedical Engineering
2025;42(4):757-765
- CountryChina
- Language:Chinese
-
Abstract:
This study aims to develop a fully automated method for tooth segmentation and root canal measurement based on cone beam computed tomography (CBCT) images, providing objective, efficient, and accurate measurement results to guide and assist clinicians in root canal diagnosis grading, instrument selection, and preoperative planning. The method utilized Attention U-Net to recognize tooth descriptors, cropped regions of interest (ROIs) based on the center of mass of these descriptors, and applied an integrated deep learning method for segmentation. The segmentation results were mapped back to the original coordinates and position-corrected, followed by automatic measurement and visualization of root canal lengths and angles. The results indicated that the Dice coefficient for segmentation was 96.42%, the Jaccard coefficient was 93.11%, the Hausdorff Distance was 2.07 mm, and the average surface distance was 0.23 mm, all of which surpassed existing methods. The relative error of the root canal working length measurement was 3.15% (< 5%), the curvature angle error was 2.85 °, and the correct classification rate of the treatment difficulty coefficient was 90.48%. The proposed methods all achieved favorable results, which can provide an important reference for clinical application.
