A relation-based framework for effective teeth recognition on dental periapical X-rays. (January 2022)

• Record Type: Journal Article
• Authors: Zhang, Kailai; Chen, Hu; Lyu, Peijun; Wu, Ji
• Is Part Of: Computerized medical imaging and graphics. Volume 95(2022)
• Publication Date: 2022-01
• Subjects: Teeth recognition -- Convolutional neural network -- Label reconstruction -- Proposal correlation module; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.102022
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Dental periapical X-rays are used as a popular tool by dentists for diagnosis. To provide dentists with diagnostic support, in this paper, we achieve automated teeth recognition of dental periapical X-rays by using deep learning techniques, including teeth location and classification. Convolutional neural network(CNN) is a popular method and has made large improvements in medical image applications. However, in our specific task, the performance of CNN is limited by lack of data and too many teeth positions in X-rays. Addressing this problem, we consider to utilize the prior dental knowledge, and therefore we propose a relation-based framework to handle the teeth location and classification task. According to the relation in teeth labels, we apply a special label reconstruction technique to decompose the teeth classification task, and use a multi-task CNN to classify the teeth positions. Meanwhile, for teeth location task, we design a proposal correlation module to use the information in teeth positions, and insert it into the multi-task CNN. A teeth sequence refinement module is used for the post processing. Our experiment results show that our relation-based framework achieves high teeth classification and location performance, which is a big improvement compared to the direct use of famous detection structures. With reliable teeth information, our method can provide automated diagnostic support for the dentists. Highlights: We propose a label reconstruction technique and design multiple branches according to the information in teeth labels. We propose a novel proposal correlation module based on the relationship of teeth positions. We propose a teeth sequence refinement module for post processing of teeth sequence. Our proposed method achieves greater performance for teeth recognition, and it can provide dentists with effective support.