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3D cephalometric landmark detection by multiple stage deep reinforcement learning

Authors
 Sung Ho Kang  ;  Kiwan Jeon  ;  Sang-Hoon Kang  ;  Sang-Hwy Lee 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 17509, 2021-09 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-09
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.
Files in This Item:
T202104432.pdf Download
DOI
10.1038/s41598-021-97116-7
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
Yonsei Authors
Lee, Sang Hwy(이상휘) ORCID logo https://orcid.org/0000-0002-9438-2489
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185954
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