Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
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Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study

  • Moe Thu Zar Aung (Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University) ;
  • Sang-Heon Lim (Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University) ;
  • Jiyong Han (Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University) ;
  • Su Yang (Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University) ;
  • Ju-Hee Kang (Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital) ;
  • Jo-Eun Kim (Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University) ;
  • Kyung-Hoe Huh (Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University) ;
  • Won-Jin Yi (Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University) ;
  • Min-Suk Heo (Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University) ;
  • Sam-Sun Lee (Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, Seoul National University)
  • Received : 2023.11.13
  • Accepted : 2024.01.10
  • Published : 2024.03.31
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Abstract

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Keywords

Acknowledgement

This work was supported by the Korea Medical Device Development Fund grant, funded by the Korean government (the Ministry of Science and ICT; the Ministry of Trade, Industry and Energy; the Ministry of Health & Welfare; and the Ministry of Food and Drug Safety) (Project Number: 1711194231, RS-2023-KD000011; Project Number: 1711174552, KMDF_PR_20200901_0147; and Project Number: 1711196792, RS-2023-00253380). This work was also supported by the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIT) (No. 2023R1A2C200532611).

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