Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data

• Published in: Oral radiology 2021-01, Vol.37 (1), p.13-19
• Main Authors: Muramatsu, Chisako, Morishita, Takumi, Takahashi, Ryo, Hayashi, Tatsuro, Nishiyama, Wataru, Ariji, Yoshiko, Zhou, Xiangrong, Hara, Takeshi, Katsumata, Akitoshi, Ariji, Eiichiro, Fujita, Hiroshi
• Format: Article
• Language: English
• Subjects: Canine teeth; Classification; Dental disorders; Dentistry; Filing; Forensic science; Image processing; Image Processing, Computer-Assisted; Imaging; Incisors; Medicine; Molars; Neural networks; Neural Networks, Computer; Oral and Maxillofacial Surgery; Original Article; Premolars; Radiography; Radiography, Panoramic; Radiology; Tooth - diagnostic imaging
• ISSN: 0911-6028; 1613-9674
• DOI: 10.1007/s11282-019-00418-w

Objectives Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases. Methods One hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies. Results The tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types. Conclusions The proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.