基于多阶段深度学习的锥形束计算机断层扫描牙齿图像分割与根管自动测量研究

Study on dental image segmentation and automatic root canal measurement based on multi-stage deep learning using cone beam computed tomography

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摘要本研究旨在开发一种基于锥形束计算机断层扫描(CBCT)图像的全自动牙齿分割和根管测量方法,提供客观、高效和准确的测量结果,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。该方法通过Attention U-Net识别牙齿描述符,根据描述符质心裁剪感兴趣区域(ROI),用集成深度学习方法分割ROI。将分割结果映射回原坐标并进行位置校正,接着自动测量根管长度和角度。分割的Dice系数为96.42%,Jaccard系数为93.11%,Hausdorff距离为2.07 mm,平均表面距离为0.23 mm,均优于现有方法。根管工作长度测量相对误差为3.15%(<5%),弯曲角度误差为2.85°,治疗难度系数分级正确率为90.48%。所提出的方法均取得较好结果,可为临床应用提供重要参考。 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.

作者陈梓晴刘奇王佳蕾吉诺龚宇航高波 CHEN Ziqing;LIU Qi;WANG Jialei;JI Nuo;GONG Yuhang;GAO Bo(School of Biomedical Engineering,Sichuan University,Chengdu 610065,P.R.China;National Clinical Research Center for Oral Diseases,West China Hospital of Stomatology,Sichuan University,Chengdu 61004l,P.R.China)

机构地区四川大学生物医学工程学院四川大学华西口腔医院国家口腔疾病临床研究中心

出处《生物医学工程学杂志》北大核心 2025年第4期757-765,共9页 Journal of Biomedical Engineering

基金四川省科技计划项目(2023NSFSC0575)。

关键词牙齿实例分割锥形束计算机断层扫描根管测量深度学习 Tooth instance segmentation Cone beam computed tomography Root canal measurement Deep learning

分类号 R78 [医药卫生—口腔医学]