Tooth recognition and classification using multi-task learning and post-processing in dental panoramic radiographs

The purpose of this study is to analyze dental panoramic radiographs for completing dental files to contribute to the diagnosis by dentists. As the initial stage, we detected each tooth and classified its tooth type. Since the final goal of this study includes multiple tasks, such as determination of dental conditions and recognition of lesions, we proposed a multitask training based on a Single Shot Multibox Detector (SSD) with a branch to predict the presence or absence of a tooth. The results showed that the proposed model improved the detection rate by 1.0%, the number of false positives per image by 0.03, and the detection rate by tooth type (total number of successfully detected and classified teeth/total number of teeth) by 1.6% compared with the original SSD, suggesting the effectiveness of the multi-task learning in dental panoramic radiographs. In addition, we integrated results of single-class detection without distinguishing the tooth type and 16-class (central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, third molar, distinguished by upper and lower jaws) detection for improving the detection rate and included post-processing for classification of teeth into 32 types and correction of tooth numbering. As a result, the detection rate of 98.8%, 0.33 false positives per image, and classification rate of 92.4% for 32 tooth types were archived.