Gu M, Thies M, Wagner F, Pechmann S, Aust O, Weidner D, Neag G, Pan Z, Utz J, Schett G, Christiansen S, Uderhardt S, Maier A (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 254-259
Conference Proceedings Title: Informatik aktuell
Event location: Braunschweig, DEU
ISBN: 9783658416560
DOI: 10.1007/978-3-658-41657-7_56
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.
APA:
Gu, M., Thies, M., Wagner, F., Pechmann, S., Aust, O., Weidner, D.,... Maier, A. (2023). Cavity Segmentation in X-ray Microscopy Scans of Mouse Tibiae. In Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 254-259). Braunschweig, DEU: Springer Science and Business Media Deutschland GmbH.
MLA:
Gu, Mingxuan, et al. "Cavity Segmentation in X-ray Microscopy Scans of Mouse Tibiae." Proceedings of the Bildverarbeitung für die Medizin Workshop, BVM 2023, Braunschweig, DEU Ed. Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2023. 254-259.
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