OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
This work addresses the need for automated dental diagnostics by providing a more accurate tool for dentists, though it is incremental as it builds on existing architectures like U-Net and YOLOv8.
This study tackled the problem of simultaneous tooth segmentation and detection in panoramic dental X-rays by introducing the OralBBNet architecture, which achieved a 1-3% improvement in mean average precision for detection and a 15-20% improvement in dice score for segmentation over state-of-the-art methods.
Teeth segmentation and recognition play a vital role in a variety of dental applications and diagnostic procedures. The integration of deep learning models has facilitated the development of precise and automated segmentation methods. Although prior research has explored teeth segmentation, not many methods have successfully performed tooth segmentation and detection simultaneously. This study presents UFBA-425, a dental dataset derived from the UFBA-UESC dataset, featuring bounding box and polygon annotations for 425 panoramic dental X-rays. In addition, this paper presents the OralBBNet architecture, which is based on the best segmentation and detection qualities of architectures such as U-Net and YOLOv8, respectively. OralBBNet is designed to improve the accuracy and robustness of tooth classification and segmentation on panoramic X-rays by leveraging the complementary strengths of U-Net and YOLOv8. Our approach achieved a 1-3% improvement in mean average precision (mAP) for tooth detection compared to existing techniques and a 15-20% improvement in the dice score for teeth segmentation over state-of-the-art (SOTA) solutions for various tooth categories and 2-4% improvement in the dice score compared to other SOTA segmentation architectures. The results of this study establish a foundation for the wider implementation of object detection models in dental diagnostics.