Chen Ziqing, Liu Qi, Wang Jialei, Ji Nuo, Gong Yuhang, Gao Bo
School of Biomedical Engineering, Sichuan University, Chengdu, China.
National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Front Bioeng Biotechnol. 2025 Jun 9;13:1565403. doi: 10.3389/fbioe.2025.1565403. eCollection 2025.
This study aims to develop a 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.
We utilizes Attention U-Net to recognize tooth descriptors, crops regions of interest (ROIs) based on the center of mass of these descriptors, and applies an integrated deep learning method for segmentation. The segmentation results are mapped back to the original coordinates and position-corrected, followed by automatic measurement and visualization of root canal lengths and angles.
Quantitative evaluation demonstrated a segmentation Dice coefficient of 96.33%, Jaccard coefficient of 92.94%, Hausdorff distance of 2.04 mm, and Average surface distance of 0.24 mm - all surpassing existing methods. The relative error of root canal length measurement was 3.42% (less than 5%), and the effect of auto-correction was recognized by clinicians.
The proposed segmentation method demonstrates favorable performance, with a relatively low relative error between automated and manual measurements, providing valuable reference for clinical applications.
本研究旨在开发一种基于锥束计算机断层扫描（CBCT）图像的牙齿分割和根管测量自动化方法，提供客观、高效且准确的测量结果，以指导和协助临床医生进行根管诊断分级、器械选择和术前规划。
我们利用注意力U-Net识别牙齿描述符，基于这些描述符的质心裁剪感兴趣区域（ROI），并应用一种集成深度学习方法进行分割。分割结果映射回原始坐标并进行位置校正，随后自动测量和可视化根管长度和角度。
定量评估显示分割的骰子系数为96.33%，杰卡德系数为92.94%，豪斯多夫距离为2.04毫米，平均表面距离为0.24毫米——均超过现有方法。根管长度测量的相对误差为3.42%（小于5%），自动校正效果得到临床医生认可。
所提出的分割方法表现出良好的性能，自动测量与手动测量之间的相对误差相对较低，为临床应用提供了有价值的参考。
