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Updated: Oct 25, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning
Michihito Nozawa1,2, Hirokazu Ito3, Yoshiko Ariji1
1Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
Objectives:
The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data.
Methods:
In total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model. The performance was evaluated using 200 images from Hospital A (internal validity test) and 200 images from Hospital B (external validity test).
Results:
Although the analysis of performance determined with data from Hospital B showed low recall (sensitivity), compared with the performance determined with data from Hospital A, both performances were above 80%. Precision (positive predictive value) was lower when test data from Hospital A were used for the position of anterior disc displacement. According to the intra-articular TMD classification, the proportions of accurately assigned TMJs were higher when using images from Hospital A than when using images from Hospital B.
Conclusion:
The segmentation deep learning model created in this study may be useful for identifying disc positions on MR images.
