Deep learning for automatic detection of cephalometric landmarks on lateral cephalometric radiographs using the Mask Region-based Convolutional Neural Network: a pilot study.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
PMID: 38480069

Abstract

OBJECTIVE: We examined the effectiveness and feasibility of the Mask Region-based Convolutional Neural Network (Mask R-CNN) for automatic detection of cephalometric landmarks on lateral cephalometric radiographs (LCRs).

Authors

  • Zhentao Jiao
    Department of Oral and Maxillofacial Surgery, Dalian Stomatological Hospital, Dalian, China; Division for Globalization Initiative, Liaison Center for Innovative Dentistry, Graduate School of Dentistry, Tohoku University, Sendai, Japan.
  • Zhuangzhuang Liang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Qian Liao
    Department of Oral and Cranio-maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai, China.
  • Sheng Chen
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • Hui Yang
    Department of Neurology, The Second Affiliated Hospital of Guizhou University of Chinese Medicine, Guiyang, China.
  • Guang Hong
    Division for Globalization Initiative, Liaison Center for Innovative Dentistry, Graduate School of Dentistry, Tohoku University, Sendai, Japan. Electronic address: hong.guang.d6@tohoku.ac.jp.
  • Haijun Gui
    Department of Oral and Cranio-maxillofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai, China. Electronic address: yuanqing860409@163.com.

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