Detection Transformer for Teeth Detection, Segmentation, and Numbering
  in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques
Hocine Kadi Théo Sourget Marzena Kawczynski Sara Bendjama Bruno Grollemund Agnès Bloch-Zupan
In this work, we focused on deep learning image processing in the context of
oral rare diseases, which pose challenges due to limited data availability. A
crucial step involves teeth detection, segmentation and numbering in panoramic
radiographs. To this end, we used a dataset consisting of 156 panoramic
radiographs from individuals with rare oral diseases and labeled by experts. We
trained the Detection Transformer (DETR) neural network for teeth detection,
segmentation, and numbering the 52 teeth classes. In addition, we used data
augmentation techniques, including geometric transformations. Finally, we
generated new panoramic images using inpainting techniques with stable
diffusion, by removing teeth from a panoramic radiograph and integrating teeth
into it. The results showed a mAP exceeding 0,69 for DETR without data
augmentation. The mAP was improved to 0,82 when data augmentation techniques
are used. Furthermore, we observed promising performances when using new
panoramic radiographs generated with inpainting technique, with mAP of 0,76.