01763nas a2200277   4500000000100000000000100001008004100002260001200043653001600055653001800071653002600089653002800115653001600143100003100159700002700190700002700217700003800244700002500282700002300307245009200330856005800422300000800480490001300488520097000501022001401471       9998                            d  c03/202510aDental CBCT10aDeep Learning10aInstance Segmentation10aMulticlass Segmentation10aTransformer1 aRafael C. Giménez-Aguilar1 aSergio Paraíso-Medina1 aMiguel García-Remesal1 aGuillermo Jesús Pradíes- Ramiro1 aMonica Bonfanti-Gris1 aRaúl Alonso-Calvo00aMulti-Class Dental CBCT Segmentation in Data-Constrained Scenarios Through Transformers  uhttps://www.ijimai.org/journal/bibcite/reference/3567  a1-90 vIn press3 aAccurate segmentation of dental structures from cone-beam computed tomography (CBCT) images has become an active research field due to the widespread use of this technology in clinical practice. In recent years, contributions have shifted from traditional computer vision methods to deep learning-based approaches. However, most of these works are based solely on convolutional neural networks (CNNs), whereas the image segmentation state-of-the-art is currently moving towards attention-based architectures. Furthermore, contributions on dental CBCTs predominantly present methods focused on a single object category, mainly teeth. In this article we tackle the segmentation of multiple oral structures by implementing previously unutilized query-based segmentation transformers. The proposed method achieves similar results to the stateof-the-art, especially on tooth segmentation, while employing a considerably smaller training dataset than prior contributions.  a1989-1660