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

Journal: BMC oral health
Published Date:

Abstract

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).

Authors

  • Jeong-Hoon Lee
  • Hee-Jin Yu
    School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul, 03722, Republic of Korea.
  • Min-Ji Kim
    Department of Orthodontics, School of Medicine, Ewha Womans University, Anyangcheon-ro 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea.
  • Jin-Woo Kim
    Department of Orthopaedic Surgery, Nowon Eulji Medical Center, Seoul, South Korea.
  • Jongeun Choi
    MSU Center for Orthopedic Research, Michigan State University, Lansing, MI, USA; School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea.