Abstract
Osteoporosis is a chronic disease that causes lower bone density and makes bones fragile. This severely impairs patient life qualities and increases the burden on the social and health care system. X-ray microscopy (XRM) allows tracking of osteoporosis-related changes at a microstructural level in the bone, entailing the characterization of osteocyte lacunae and blood vessel canals. Unfortunately, no segmentation methods for micro-structures in XRM images have yet been established. In this work, we compare the performance of a traditional thresholding-based method with three deep learning networks including 2D and 3D models in both binary and multi-class segmentation. We further propose a clustering method to automatically distinguish blood vessels from lacunae for the binary methods. The performance is evaluated with Dice score (F1 score). The thresholding-based method reaches a mean Dice score of 0.729, which the deep learning models improve by 0.129 - 0.168.
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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Gu, M. et al. (2023). Cavity Segmentation in X-ray Microscopy Scans of Mouse Tibiae. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_56
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DOI: https://doi.org/10.1007/978-3-658-41657-7_56
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