Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

Jeong Hoon Lee, Hee Jin Yu, Min Ji Kim, Jin Woo Kim, Jongeun Choi

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78 Scopus citations


Background: Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). Methods: We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. Results: Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. Conclusion: Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

Original languageEnglish
Article number270
JournalBMC Oral Health
Issue number1
StatePublished - 7 Oct 2020

Bibliographical note

Funding Information:
The work of Jeong-Hoon Lee, Hee-Jin Yu and Jongeun Choi was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1A4A1025986) and by the Mid-career Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A2B6008063). The work of Jin-Woo Kim was supported by the Ewha Womans University Research Grant of 2019 (1–2019–1914-001-1). The funding bodies had no role in the design of the study, data collection, analysis, interpretation of data and writing the manuscript.

Publisher Copyright:
© 2020 The Author(s).


  • Artificial intelligence
  • Artificial neural networks
  • Bayesian method
  • Cephalometry
  • Deep learning
  • Dental anatomy
  • Machine vision
  • Oral & maxillofacial surgery
  • Orthodontic(s)
  • Orthodontics
  • Orthognathic/orthognathic surgery
  • Radiography


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