Locating Anatomical Landmarks on 2D Lateral Cephalograms Through Adversarial Encoder-Decoder Networks

Locating anatomical landmarks in a cephalometric X-ray image is a crucial step in cephalometric analysis. Manual landmark localization suffers from inter- and intra-observer variability, which makes developing automated localization methods urgent in clinics. Most of the existing techniques follow the routine thoughts which estimate numerical values of displacements or coordinates for the target landmarks. Additionally, there are no reported applications of generative adversarial networks (GAN) in cephalometric landmark localization. Motivated by these facts, we propose a new automated cephalometric landmark localization method under the framework of GAN. The principle behind our approach is fundamentally different from the conventional ones. It trained an adversarial network under the framework of GAN to learn the mapping from features to the distance map of a specific target landmark. Namely, the output of the adversarial network in this paper is image data, instead of displacements or coordinates as the conventional approaches. Based on the trained networks, we can predict the distance maps of all target landmarks in a new cephalometric image. Subsequently, the target landmarks are detected from the predicted distance maps by an approach similar to regression voting. Experimental results validate the good performance of our method in localization of cephalometric landmarks in dental X-ray images.