Landmarking of CT scans is an important step in the alignment of skulls that is key in surgery planning, pre-/post-surgery comparisons, and morphometric studies. We present a novel method for automatically locating anatomical landmarks on the surface of cone beam CT-based image models of human skulls using 2D Gabor wavelets and ensemble learning. The algorithm is validated via human inter- and intra-rater comparisons on a set of 39 scans and a skull superimposition experiment with an established surgery planning software (Maxilim). Automatic landmarking results in an accuracy of 1-2 mm for a subset of landmarks around the nose area as compared to a gold standard derived from human raters. These landmarks are located in eye sockets and lower jaw, which is competitive with or surpasses inter-rater variability. The well-performing landmark subsets allow for the automation of skull superimposition in clinical applications. Our approach delivers accurate results, has modest training requirements (training set size of 30-40 items) and is generic, so that landmark sets can be easily expanded or modified to accommodate shifting landmark interests, which are important requirements for the landmarking of larger cohorts.

automated landmarking, ensemble methods, Gabor wavelet, human skull, surface scan, tomography,
Physics in Medicine and Biology
Department of Oral and Maxillofacial Surgery

de Jong, M.A, Gül, A, de Gijt, J.P, Koudstaal, M.J, Kayser, M.H, Wolvius, E.B, & Böhringer, S. (2018). Automated human skull landmarking with 2D Gabor wavelets. Physics in Medicine and Biology, 63(10). doi:10.1088/1361-6560/aabfa0