Accurate mandibular canal segmentation of dental CBCT using a two-stage 3D-UNet based segmentation framework

Xi Lin; Weini Xin; Jingna Huang; Yang Jing; Pengfei Liu; Jingdan Han; Jie Ji

doi:10.1186/s12903-023-03279-2

Accurate mandibular canal segmentation of dental CBCT using a two-stage 3D-UNet based segmentation framework

BMC Oral Health. 2023 Aug 10;23(1):551. doi: 10.1186/s12903-023-03279-2.

Authors

Xi Lin¹, Weini Xin^{2

3}, Jingna Huang¹, Yang Jing⁴, Pengfei Liu⁴, Jingdan Han⁴, Jie Ji⁵

Affiliations

¹ Clinic of Stomatology of the Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong, China.
² Clinic of Stomatology of the Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong, China. xin_weini@foxmail.com.
³ Department of Stomatology of Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangddong, China. xin_weini@foxmail.com.
⁴ Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing, China.
⁵ Network and Information Center, Shantou University, No. 243, University Road, Shantou, Guangdong, China.

Abstract

Objectives: The objective of this study is to develop a deep learning (DL) model for fast and accurate mandibular canal (MC) segmentation on cone beam computed tomography (CBCT).

Methods: A total of 220 CBCT scans from dentate subjects needing oral surgery were used in this study. The segmentation ground truth is annotated and reviewed by two senior dentists. All patients were randomly splitted into a training dataset (n = 132), a validation dataset (n = 44) and a test dataset (n = 44). We proposed a two-stage 3D-UNet based segmentation framework for automated MC segmentation on CBCT. The Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD) were used as the evaluation metrics for the segmentation model.

Results: The two-stage 3D-UNet model successfully segmented the MC on CBCT images. In the test dataset, the mean DSC was 0.875 ± 0.045 and the mean 95% HD was 0.442 ± 0.379.

Conclusions: This automatic DL method might aid in the detection of MC and assist dental practitioners to set up treatment plans for oral surgery evolved MC.

Keywords: Artificial intelligence; Cone-beam computerized tomography; Dental radiology; Inferior alveolar nerve.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cone-Beam Computed Tomography / methods
Dentists
Humans
Image Processing, Computer-Assisted* / methods
Mandibular Canal
Professional Role
Spiral Cone-Beam Computed Tomography*