实现 CBCT 下颌管临床应用的自动分割

Towards clinically applicable automated mandibular canal segmentation on CBCT.

机构信息

Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China.

Deepcare, Inc, Beijing, China.

出版信息

J Dent. 2024 May;144:104931. doi: 10.1016/j.jdent.2024.104931. Epub 2024 Mar 6.

DOI:10.1016/j.jdent.2024.104931

PMID:38458378

Abstract

OBJECTIVES

To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images.

METHODS

The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net.

RESULTS

The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm).

CONCLUSIONS

These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization.

CLINICAL SIGNIFICANCE

Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.

摘要

目的

开发一种基于深度学习的系统，用于对锥形束计算机断层扫描（CBCT）图像中的下颌管进行精确、稳健和全自动分割。

方法

该系统是在一个中心的 536 个 CBCT 扫描（训练集：376，验证集：80，测试集：80）上开发的，并在来自 3 个中心的 89 个 CBCT 扫描的外部数据集上进行了验证。每个扫描都使用多阶段注释方法进行注释，并由口腔颌面放射科医生进行细化。我们提出了一个三步骤策略用于下颌管分割：基于 2D U-Net 的感兴趣区域提取、下颌管的全局分割以及基于 3D U-Net 的分割细化。

结果

该系统在内部数据集（Dice 相似系数[DSC]，0.952；交并比[IoU]，0.912；平均对称表面距离[ASSD]，0.046mm；95%Hausdorff 距离[HD95]，0.325mm）和外部数据集（DSC，0.960；IoU，0.924；ASSD，0.040mm；HD95，0.288mm）中均实现了准确的下颌管分割。