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Learning-based local-to-global landmark annotation for automatic 3D cephalometry

Authors
 Hye Sun Yun  ;  Tae Jun Jang  ;  Sung Min Lee  ;  Sang-Hwy Lee  ;  Jin Keun Seo 
Citation
 PHYSICS IN MEDICINE AND BIOLOGY, Vol.65(8) : 085018, 2020-04 
Journal Title
 PHYSICS IN MEDICINE AND BIOLOGY 
ISSN
 0031-9155 
Issue Date
2020-04
Abstract
The annotation of three-dimensional (3D) cephalometric landmarks in 3D computerized tomography (CT) has become an essential part of cephalometric analysis, which is used for diagnosis, surgical planning, and treatment evaluation. The automation of 3D landmarking with high-precision remains challenging due to the limited availability of training data and the high computational burden. This paper addresses these challenges by proposing a hierarchical deep-learning method consisting of four stages: 1) a basic landmark annotator for 3D skull pose normalization, 2) a deep-learning-based coarse-to-fine landmark annotator on the midsagittal plane, 3) a low-dimensional representation of the total number of landmarks using variational autoencoder (VAE), and 4) a local-to-global landmark annotator. The implementation of the VAE allows two-dimensional-image-based 3D morphological feature learning and similarity/dissimilarity representation learning of the concatenated vectors of cephalometric landmarks. The proposed method achieves an average 3D point-to-point error of 3.63 mm for 93 cephalometric landmarks using a small number of training CT datasets. Notably, the VAE captures variations of craniofacial structural characteristics.
Full Text
https://iopscience.iop.org/article/10.1088/1361-6560/ab7a71
DOI
10.1088/1361-6560/ab7a71
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/22282913/179156
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