MorphMaskFormer: a transformer-based deep segmentation model for multi-class Demirjian stage estimation from panoramic radiographs

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dc.contributor.authorKıranşal, Melike
dc.contributor.authorÖzçelik, Salih Talha Alperen
dc.contributor.authorAydan, Tuba
dc.contributor.authorÜzen, Hüseyin
dc.contributor.authorFırat, Hüseyin
dc.contributor.authorŞengür, Abdulkadir
dc.contributor.authorAbdelkarim, Ahmed Z.
dc.date.accessioned2026-04-04T13:18:55Z
dc.date.available2026-04-04T13:18:55Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractObjectives 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. © 2026 Elsevier Inc.
dc.identifier.doi10.1016/j.oooo.2026.01.012
dc.identifier.issn2212-4403
dc.identifier.scopus2-s2.0-105033043037
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.oooo.2026.01.012
dc.identifier.urihttps://hdl.handle.net/11616/108016
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Inc.
dc.relation.ispartofOral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.titleMorphMaskFormer: a transformer-based deep segmentation model for multi-class Demirjian stage estimation from panoramic radiographs
dc.typeArticle

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