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Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

SHENG Chen1,2, WANG Lin1,2,3, HUANG Zhenhuan1,2,3, WANG Tian1,2,3, GUO Yalin1,2,3, HOU Wenjie1,2,3, XU Laiqing1,2,3, WANG Jiazhu1,2,3, YAN Xue1,2,3   

  1. 1. Medical School of Chinese PLA, Beijing 100853, China;
    2. Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing 100853, China;
    3. Beihang University, Beijing 100191, China
  • Received:2022-01-24 Revised:2022-03-23 Online:2023-01-25 Published:2023-02-09

SHENG Chen, WANG Lin, HUANG Zhenhuan, WANG Tian, GUO Yalin, HOU Wenjie, XU Laiqing, WANG Jiazhu, YAN Xue. Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs[J]. Journal of Systems Science and Complexity, 2023, 36(1): 257-272.

Panoramic radiographs can assist dentist to quickly evaluate patients’ overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWinUnet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, LinkNet and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.
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