26 105

Cited 0 times in

A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography

 Hye Sun Yun  ;  Chang Min Hyun  ;  Seong Hyeon Baek  ;  Sang-Hwy Lee  ;  Jin Keun Seo 
 PLOS ONE, Vol.17(9) : e0275114, 2022-09 
Journal Title
Issue Date
Anatomic Landmarks* / anatomy & histology ; Anatomic Landmarks* / diagnostic imaging ; Cephalometry / methods ; Humans ; Imaging, Three-Dimensional* / methods ; Reproducibility of Results ; Supervised Machine Learning ; Tomography, X-Ray Computed
Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D computed tomography (CT) images is a cumbersome task even for the trained experts, automatic 3D landmark detection system is in a great need. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level, because of the factors hindering machine learning such as the high dimensionality of the input data and limited amount of training data due to the ethical restrictions on the use of medical data. This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed. The proposed method first detects a small number of easy-to-find reference landmarks, then uses them to provide a rough estimation of the all landmarks by utilizing the low dimensional representation learned by variational autoencoder (VAE). The anonymized landmark dataset is used for training the VAE. Finally, coarse-to-fine detection is applied to the small bounding box provided by rough estimation, using separate strategies suitable for the mandible and the cranium. For mandibular landmarks, patch-based 3D CNN is applied to the segmented image of the mandible (separated from the maxilla), in order to capture 3D morphological features of mandible associated with the landmarks. We detect 6 landmarks around the condyle all at once rather than one by one, because they are closely related to each other. For cranial landmarks, we again use the VAE-based latent representation for more accurate annotation. In our experiment, the proposed method achieved a mean detection error of 2.88 mm for 90 landmarks using only 15 paired training data.
Files in This Item:
T202205750.pdf Download
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
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.