The automatic segmentation of the temporomandibular joint based on MRI using deep learning method
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(...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Stomatology
2025-06-01
|
| Series: | Kouqiang yixue |
| Subjects: | |
| Online Access: | https://www.stomatology.cn/fileup/1003-9872/PDF/1751968164346-254688385.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321144581095424 |
|---|---|
| author | LIU Fei, ZHANG Jiulou, JIN Ruofan, ZHANG Nan, ZHOU Weina |
| author_facet | LIU Fei, ZHANG Jiulou, JIN Ruofan, ZHANG Nan, ZHOU Weina |
| author_sort | LIU Fei, ZHANG Jiulou, JIN Ruofan, ZHANG Nan, ZHOU Weina |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1492d0838cac440e9cc575ffccb1cce9 |
| institution | Kabale University |
| issn | 1003-9872 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Stomatology |
| record_format | Article |
| series | Kouqiang yixue |
| spelling | doaj-art-1492d0838cac440e9cc575ffccb1cce92025-08-20T03:49:50ZzhoEditorial Office of StomatologyKouqiang yixue1003-98722025-06-0145644545210.13591/j.cnki.kqyx.2025.06.009The automatic segmentation of the temporomandibular joint based on MRI using deep learning methodLIU Fei, ZHANG Jiulou, JIN Ruofan, ZHANG Nan, ZHOU Weina0Department of TMD & Orofacial Pain, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing 210029, ChinaObjective 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.https://www.stomatology.cn/fileup/1003-9872/PDF/1751968164346-254688385.pdf|temporomandibular joint|magnetic resonance imaging|deep learning|automatic segmentation |
| spellingShingle | LIU Fei, ZHANG Jiulou, JIN Ruofan, ZHANG Nan, ZHOU Weina The automatic segmentation of the temporomandibular joint based on MRI using deep learning method Kouqiang yixue |temporomandibular joint|magnetic resonance imaging|deep learning|automatic segmentation |
| title | The automatic segmentation of the temporomandibular joint based on MRI using deep learning method |
| title_full | The automatic segmentation of the temporomandibular joint based on MRI using deep learning method |
| title_fullStr | The automatic segmentation of the temporomandibular joint based on MRI using deep learning method |
| title_full_unstemmed | The automatic segmentation of the temporomandibular joint based on MRI using deep learning method |
| title_short | The automatic segmentation of the temporomandibular joint based on MRI using deep learning method |
| title_sort | automatic segmentation of the temporomandibular joint based on mri using deep learning method |
| topic | |temporomandibular joint|magnetic resonance imaging|deep learning|automatic segmentation |
| url | https://www.stomatology.cn/fileup/1003-9872/PDF/1751968164346-254688385.pdf |
| work_keys_str_mv | AT liufeizhangjiuloujinruofanzhangnanzhouweina theautomaticsegmentationofthetemporomandibularjointbasedonmriusingdeeplearningmethod AT liufeizhangjiuloujinruofanzhangnanzhouweina automaticsegmentationofthetemporomandibularjointbasedonmriusingdeeplearningmethod |