Automated 3D cephalometric landmark identification using computerized tomography
This research provides an automated solution for identifying 3D cephalometric landmarks, which can significantly reduce the cumbersome manual landmarking task for orthodontists and radiologists, even with limited medical imaging data.
This paper addresses the challenge of automated 3D cephalometric landmark identification from CT images, which is crucial for cephalometric analysis but difficult due to high data dimensionality and limited training data. The proposed semi-supervised deep learning method achieved an average 3D point-to-point error of 2.91 mm for 90 landmarks using only 15 paired training datasets.
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.