Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural network features

Dental image analysis is important for orthodontics, forensics, and dental treatments like cavity restoration or implants. In order to build a computer-aided diagnosis system for dental analysis, localization and numbering of teeth are crucial. In this study, we propose to use a popular deep learning technique, Mask regions with convolutional neural network features (RCNN), for simultaneous detection, segmentation, and numbering of teeth in panoramic X-ray images. Multiclass labeling is performed by Mask RCNN by giving a unique class name to each tooth type. After classification, postprocessing is performed for numbering teeth according to detected labels and dental chart. The proposed method is trained on 200 images and tested on 278 panoramic dental images. The average tooth detection accuracy is 0.98, and F1 score for segmentation is 0.93.