3D cephalometric landmark detection by multiple stage deep reinforcement learning

Sung Ho Kang, Kiwan Jeon, Sang Hoon Kang, Sang Hwy Lee

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.

Original languageEnglish
Article number17509
JournalScientific reports
Volume11
Issue number1
DOIs
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This research was supported by a grant from the Korea Health Technology R&D Project, funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number HI20C0127) for S.-H.L., S.H.K., and K.J. S.H.K. and K.J. were partially supported by the National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government (No. NIMS-B21910000).

Publisher Copyright:
© 2021, The Author(s).

All Science Journal Classification (ASJC) codes

  • General

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