期刊论文详细信息
BMC Oral Health
Accurate mandibular canal segmentation of dental CBCT using a two-stage 3D-UNet based segmentation framework
Research
Xi Lin1  Jingna Huang1  Weini Xin2  Yang Jing3  Jingdan Han3  Pengfei Liu3  JieJi4 
[1] Clinic of Stomatology of the Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong, China;Clinic of Stomatology of the Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong, China;Department of Stomatology of Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangddong, China;Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing, China;Network and Information Center, Shantou University, No. 243, University Road, Shantou, Guangdong, China;
关键词: Artificial intelligence;    Dental radiology;    Cone-beam computerized tomography;    Inferior alveolar nerve;   
DOI  :  10.1186/s12903-023-03279-2
 received in 2022-11-06, accepted in 2023-08-02,  发布年份 2023
来源: Springer
PDF
【 摘 要 】

ObjectivesThe 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).MethodsA 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.ResultsThe 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.ConclusionsThis automatic DL method might aid in the detection of MC and assist dental practitioners to set up treatment plans for oral surgery evolved MC.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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