Oral surgery, oral medicine, oral pathology and oral radiology · 2026
MorphMaskFormer: a transformer-based deep segmentation model for multi-class Demirjian stage estimation from panoramic radiographs.
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Kıranşal Melike, Özçelik Salih Talha Alperen, Aydan Tuba, Üzen Hüseyin, Fırat Hüseyin, Şengür Abdulkadir, Abdelkarim Ahmed Z, Duman Şuayip Burak
Аннотация
OBJECTIVES: This study aims to develop an advanced deep learning model that automatically determines third-molar developmental stages in panoramic radiographs using the Demirjian classification, improving the accuracy and objectivity of dental age estimation for forensic and clinical applications.
STUDY DESIGN: A total of 888 panoramic radiographs from individuals aged 7 to 30 were annotated by 2 experts based on Demirjian's A-H staging system. The proposed model, MorphMaskFormer, is built upon the classical UNet architecture, incorporating a lightweight transformer attention module inspired by Mask2Former. The model performs both binary (tooth/background) and multi-class (A-H stages) segmentation. Its performance was evaluated using IoU, Dice coefficient, Precision, Recall, and inference time, and compared against UNet, ResUNet, DeepLabV3+, PSPNet, and SegNet.
RESULTS: MorphMaskFormer outperformed all baseline models, achieving a Dice score of 0.9461, IoU of 0.8985, and the fastest inference time at 78.59 ms. In multi-class segmentation, it showed high accuracy for stages A, D, and H, with an overall component accuracy of 72.41%.
CONCLUSIONS: MorphMaskFormer enables precise pixel-level segmentation of dental developmental stages, reducing inter-observer variability and shortening evaluation time. Its high accuracy and efficiency make it a scalable tool that enhances diagnostic confidence and supports critical clinical and forensic age-estimation decisions.
STUDY DESIGN: A total of 888 panoramic radiographs from individuals aged 7 to 30 were annotated by 2 experts based on Demirjian's A-H staging system. The proposed model, MorphMaskFormer, is built upon the classical UNet architecture, incorporating a lightweight transformer attention module inspired by Mask2Former. The model performs both binary (tooth/background) and multi-class (A-H stages) segmentation. Its performance was evaluated using IoU, Dice coefficient, Precision, Recall, and inference time, and compared against UNet, ResUNet, DeepLabV3+, PSPNet, and SegNet.
RESULTS: MorphMaskFormer outperformed all baseline models, achieving a Dice score of 0.9461, IoU of 0.8985, and the fastest inference time at 78.59 ms. In multi-class segmentation, it showed high accuracy for stages A, D, and H, with an overall component accuracy of 72.41%.
CONCLUSIONS: MorphMaskFormer enables precise pixel-level segmentation of dental developmental stages, reducing inter-observer variability and shortening evaluation time. Its high accuracy and efficiency make it a scalable tool that enhances diagnostic confidence and supports critical clinical and forensic age-estimation decisions.