Automatic extraction of endocranial surfaces from CT images of crania

Abstract

The authors present a method for extracting polygon data of endocranial surfaces from CT images of human crania. Based on the fact that the endocast is the largest empty space in the crania, we automate a procedure for endocast extraction by integrating several image processing techniques. Given CT images of human crania, the proposed method extracts endocranial surfaces by the following three steps. The first step is binarization in order to fill void structures, such as diploic space and cracks in the skull. We use a void detection method based on mathematical morphology. The second step is watershed-based segmentation of the endocranial part from the binary image of the CT image. Here, we introduce an automatic initial seed assignment method for the endocranial region using the distance field of the binary image. The final step is partial polygonization of the CT images using the segmentation results as mask images. The resulting polygons represent only the endocranial part, and the closed manifold surfaces are computed even though the endocast is not isolated in the cranium. Since only the isovalue threshold and the size of void structures are required, the procedure is not dependent on the experience of the user. The present paper also demonstrates that the proposed method can extract polygon data of endocasts from CT images of various crania.

Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools

“…As compared to manual protocols (i.e., visible surface cutting‐out and segmentation) (Ogihara et al, ), CA‐LSE and AST‐3D are faster and more accurate, avoiding biases due to the subjectivity of an operator performing a manual segmentation. With respect to other semi‐automatic tools (e.g., see Michikawa et al, ) which use segmentation of CT‐scan instead of mesh editing, the entire procedure is easier, more versatile, and allows to build, at once, the meshes of both the external topology and internal structures.…”

Are endocasts reliable proxies for brains? A 3D quantitative comparison of the extant human brain and endocast

“…More specifically, in the last decades, software that are now widely used in neurosciences have been developed for virtually manipulating, automatically segmenting (e.g., regional segmentation of the brain), identifying and analysing neuroanatomical features in brains from volumetric image data (e.g., Borne et al, 2020; Reuter et al, 2012; Rivière et al, 2009). Similarly, automatic segmentation methods for generating virtual endocasts are now available in paleosciences (e.g., Endex, Endomarker, Michikawa et al, 2017; Profico et al, 2020; Subsol et al, 2010). However, analytical tools for the automatic recognition and identification of cerebral imprints in endocasts are still scarce (e.g., automatic detection of sulcal imprints, Beaudet et al, 2016, 2019a; de Jager et al, 2019).…”

Retrospective Investigation of Cranial Volume and Cephalic Index in Patients with Nonsyndromic Sagittal Synostosis Operated by Total Vault Remodeling

“…In this study, Michikawa et al's method for automatic extraction of endocranial surfaces from CT images served as the basis for the ICV measurement [26]. This method used the three major steps of binarization, segmentation, and polygonization.…”