Abstract
This study introduces a semi-automatic method to segment brain tissue from background in stacks of registered 2D images collected during histological sectioning. It is designed for setups where automatic segmentation algorithms often fail. It facilitates a manual process by providing an efficient interpolation between image masks, thus requiring only a subset of images to be manually segmented. Assuming that images are already correctly registered one to another, interpolation is done by morphing between existing masks based on Laplace’s equation, derived from a well established model for mapping cortical thickness. We applied the proposed method successfully to segment whole brain image stacks with less than 10% of manually segmented sections. The results can be used as an input for subsequent high-level segmentation steps.
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Schober, M., Axer, M., Huysegoms, M., Schubert, N., Amunts, K., Dickscheid, T. (2016). Morphing Image Masks for Stacked Histological Sections Using Laplace’s Equation. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2016. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49465-3_27
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DOI: https://doi.org/10.1007/978-3-662-49465-3_27
Publisher Name: Springer Vieweg, Berlin, Heidelberg
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