The automatic segmentation of the temporomandibular joint based on MRI using deep learning method
10.13591/j.cnki.kqyx.2025.06.009
- VernacularTitle:基于深度学习方法建立颞下颌关节核磁共振影像的自动分割模型
- Author:
Fei LIU
1
;
Jiulou ZHANG
;
Ruofan JIN
;
Nan ZHANG
;
Weina ZHOU
Author Information
1. 南京医科大学附属口腔医院颞颌关节与颌面疼痛科,江苏南京(210029);口腔疾病研究与防治国家级重点实验室培育建设点,江苏南京(210029);江苏省口腔转化医学工程研究中心,江苏南京(210029)
- Publication Type:Journal Article
- Keywords:
temporomandibular joint;
magnetic resonance imaging;
deep learning;
automatic segmentation
- From:
STOMATOLOGY
2025;45(6):445-452
- CountryChina
- Language:Chinese
-
Abstract:
Objective To build an automatic segmentation model of temporomandibular joint(TMJ)based on magnetic resonance im-aging(MRI)using deep learning method.Methods The MRI data of TMJ of 104 subjects were collected,with the articular disc,con-dyle and glenoid fossa marked.The adaptive U-Net framework(nnU-Net)was used to construct a segmentation model,which was sub-jected to both quantitative and qualitative assessments.Results The segmentation model demonstrated excellent accuracy in segmenta-tion.In the segmentation of different joint structures,the model achieved Dice of 0.77 for the articular disc,0.85 for the condyle,and 0.66 for the glenoid fossa.The model showed similar segmentation performance when processing MRI images in both open-mouth and closed-mouth states.Conclusion This study developed an automatic segmentation model for TMJ MRI based on deep learning,which can assist clinicians in diagnosing anterior displacement of the TMJ disc.
