  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 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 entire landmarks
by utilizing the low dimensional representation learned by variational
autoencoder (VAE). 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 mandible and
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, instead of one by one,
because they are closely related to each other. For cranial landmarks, we again
use VAE-based latent representation for more accurate annotation. In our
experiment, the proposed method achieved an averaged 3D point-to-point error of
2.91 mm for 90 landmarks only with 15 paired training data.
