Craniofacial disorders are routinely diagnosed using computed tomography imaging. Corrective surgery is often performed early in life to restore the skull to a more normal shape. In order to quantitatively assess the shape change due to surgery, we present an automated method for intracranial space segmentation. The method utilizes a two-stage approach which firstly initializes the segmentation with a cascade of mathematical morphology operations. This segmentation is then refined with a level-set-based approach that ensures that low-contrast boundaries, where bone is absent, are completed smoothly. We demonstrate this method on a dataset of 43 images and show that the method produces consistent and accurate results.
Adamson, C., da Costa, A. C., Beare, R., & Wood, A. G. (2013). Automatic intracranial space segmentation for computed tomography brain images. Journal of Digital Imaging, 26(3), 563-571. https://doi.org/10.1007/s10278-012-9529-8