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Updated: Jul 12, 2026
Automated Joint Space Detection Improves Bone Segmentation Accuracy
Published on: November 28, 2025
Automated multi-orientation (plane-wise) segmentation of TMJ structures using deep learning: a dual-center study
Wenqi Yang1, Yaqiong Zhang1, Yunying Zhao2
1Department of Radiology, Shanghai Stomatological Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Fudan University, Shanghai, China.
Objective:
To develop and validate a deep learning framework for automated multi-view (across planes) MRI segmentation of temporomandibular joint (TMJ) structures, reducing subjectivity and improving efficiency, and supporting future quantitative analyses (e.g., geometry/volume measurements) pending dedicated clinical validation.
Study Design:
Two-center MRI data from 121 (242 TMJs) included oblique coronal/sagittal sequences (closed/open-mouth). Four models (2D U-Net, 3D U-Net, nnU-Net, Swin-Unet) were trained on 65 patients and tested on two independent sets of 28 patients each for segmenting the temporal bone articular surface, articular disc, and condyle. Performance was evaluated using Dice similarity coefficient, intraclass correlation coefficients (ICCs), and Wilcoxon tests.
Results:
All models showed high agreement with manual annotations (ICC > 0.90, P < .05). nnU-Net achieved superior performance (ICC > 0.99 across structures), with condyle Dice = 0.949 ± 0.020 (95% CI: 0.942-0.956) and disc Dice = 0.865 ± 0.042 (95% CI: 0.850-0.880). Performance was consistent across imaging planes (error<5%).
Conclusions:
The nnU-Net-based framework enables accurate, computationally feasible, and robust multi-view (across-plane) TMJ segmentation. Dual-center evaluation across institutions with similar protocols supports technical reproducibility, while broader generalizability and clinical utility require further multi-scanner and outcome-based validation.
