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Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks: a developmental trial

Authors
 Sung Ho Kang  ;  Kiwan Jeon  ;  Hak-Jin Kim  ;  Jin Keun Seo  ;  Sang-Hwy Lee 
Citation
 Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization, Vol.8(2) : 210-218, 2020 
Journal Title
 Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization 
ISSN
 2168-1163 
Issue Date
2020
Keywords
Cephalometry ; annotation ; deep learning ; convolutional neural network ; three-dimensional ; computed tomography
Abstract
Automatic annotation for three-dimensional (3D) cephalometric analysis has been limited by computational complexity and computing performance. The purpose of this study was to evaluate the accuracy of our newly-developed automatic 3D cephalometric annotation system using a deep learning algorithm. Our model system mainly consisted of a 3D convolutional neural network and image data resampling. Discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to yield prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference (p > 0.05) among the landmarks of three groups (midsagittal plane, horizontal plane and mandible). Although our 3D convolutional neural network-based annotation system could not achieve the level of accuracy demanded by immediate clinical applications, it can nevertheless serve as an initial approximate guide to landmarks, thus reducing the time needed for annotation.
Full Text
https://www.tandfonline.com/doi/full/10.1080/21681163.2019.1674696
DOI
10.1080/21681163.2019.1674696
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hak Jin(김학진) ORCID logo https://orcid.org/0000-0001-6063-3406
Lee, Sang Hwy(이상휘) ORCID logo https://orcid.org/0000-0002-9438-2489
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/175524
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