Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
Pediatric dental segmentation is critical in dental diagnostics, presenting
unique challenges due to variations in dental structures and the lower number
of pediatric X-ray images. This study proposes a custom SegUNet model with a
VGG19 backbone, designed explicitly for pediatric dental segmentation and
applied to the Children's Dental Panoramic Radiographs dataset. The SegUNet
architecture with a VGG19 backbone has been employed on this dataset for the
first time, achieving state-of-the-art performance. The model reached an
accuracy of 97.53%, a dice coefficient of 92.49%, and an intersection over
union (IOU) of 91.46%, setting a new benchmark for this dataset. These results
demonstrate the effectiveness of the VGG19 backbone in enhancing feature
extraction and improving segmentation precision. Comprehensive evaluations
across metrics, including precision, recall, and specificity, indicate the
robustness of this approach. The model's ability to generalize across diverse
dental structures makes it a valuable tool for clinical applications in
pediatric dental care. It offers a reliable and efficient solution for
automated dental diagnostics.
