Multi-Class Dental CBCT Segmentation in Data-Constrained Scenarios Through Transformers

Accurate 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.