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.

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Keywords automated landmarking, ensemble methods, Gabor wavelet, human skull, surface scan, tomography
Persistent URL dx.doi.org/10.1088/1361-6560/aabfa0, hdl.handle.net/1765/107411
Journal Physics in Medicine and Biology
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Citation
De Jong, M.A. (Markus A.), Gül, A. (Atilla), 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