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  • [Phys Med Biol.] Learning-based local-to-global landmark annotation for automatic 3D cephalometry.

    연세대 / 윤혜선, 이상휘*

  • 출처
    Phys Med Biol.
  • 등재일
    2020 Apr 23
  • 저널이슈번호
    65(8):085018. doi: 10.1088/1361-6560/ab7a71.
  • 내용

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    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.

     


    Author information

    Yun HS1, Jang TJ, Lee SM, Lee SH, Seo JK.
    1
    Department of Computational Science and Engineering, Yonsei University, Seoul, Republic of Korea.

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