Automatic landmark prediction in craniofacial CT images

Three dimensional coordinates prediction of landmarks on craniofacial bone

Abstract

Objective: X-ray CT images of the craniofacial region are now routinely used in the diagnosis and treatment of craniofacial conditions. 3D reconstruction is possible, and the distance between landmarks and the angle between reference planes can be measured in 3D. In order to do so, it is necessary to plot the landmarks, but this requires time and experience. In this study, we attempted to predict the 3D coordinates of feature points from a set of CT images by using two-phased deep learning networks. Methods: In the first phase, the DICOM image set was compressed and a model was trained. In the second phase, 3D images around the coordinates of each landmark were cropped with original resolution and models were trained. Results: The prediction error was smaller when the estimation was done in two phases than when it was done only in the first phase.