Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.

Journal: Sensors (Basel, Switzerland)
PMID: 33445758

Abstract

This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preprocessing phase. The intra-examiner reliability was high (ICC = 0.99) in manual identification. The results of prediction of the system for average mean radial error (MRE) and standard deviation (SD) were 1.03 mm and 1.29 mm, respectively. In conclusion, different types of image data might be the one of factors that affect the prediction accuracy of a fully-automated landmark identification system, based on multi-stage CNNs.

Authors

  • Min-Jung Kim
    Department of Orthodontics, Graduate School, Kyung Hee University, Seoul 02447, Korea.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Song Hee Oh
    Department of Oral and Maxillofacial Radiology, Graduate School, Kyung Hee University, Seoul 02447, Korea.
  • Hyo-Won Ahn
    Department of Orthodontics, Graduate School, Kyung Hee University, Seoul 02447, Korea.
  • Seong-Hun Kim
    Department of Orthodontics, Graduate School, Kyung Hee University, Seoul 02447, Korea.
  • Gerald Nelson
    Division of Orthodontics, Department of Orofacial Science, University of California San Francisco, San Francisco, CA 94143, USA.

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