Stomatology ›› 2025, Vol. 45 ›› Issue (6): 445-452.doi: 10.13591/j.cnki.kqyx.2025.06.009

• Basic and Clinical Research • Previous Articles     Next Articles

The automatic segmentation of the temporomandibular joint based on MRI using deep learning method

LIU Fei1,2,3, ZHANG Jiulou4(), JIN Ruofan1,2,3, ZHANG Nan1,2,3, ZHOU Weina1,2,3()   

  1. Department of TMD & Orofacial Pain, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing 210029, China
  • Received:2024-12-29 Online:2025-06-28 Published:2025-07-08

Abstract:

Objective To build an automatic segmentation model of temporomandibular joint(TMJ) based on magnetic resonance imaging(MRI) using deep learning method. Methods The MRI data of TMJ of 104 subjects were collected, with the articular disc, condyle and glenoid fossa marked. The adaptive U-Net framework(nnU-Net) was used to construct a segmentation model, which was subjected to both quantitative and qualitative assessments. Results The segmentation model demonstrated excellent accuracy in segmentation. 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.

Key words: temporomandibular joint, magnetic resonance imaging, deep learning, automatic segmentation

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