Abstract
For rupture risk assessment of intracranial aneurysms, 3D surface model extraction might be time-consuming but supports calculation of morphological and hemodynamical parameters. We present a geometric deep learning approach to segment the intracranial vascular domain of interest in 3D surface meshes comprising only the aneurysm and surrounding vessels to speed up this process. This fast and automatic segmentation supports mesh cutting needed for further mesh processing and subsequent rupture risk assessment.With deep learning on patient-specific 3D geometries, the vascular domain could be segmented with an accuracy of 88 percent.
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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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Kreher, R. et al. (2023). Geometric Deep Learning Vascular Domain Segmentation. 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_34
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DOI: https://doi.org/10.1007/978-3-658-41657-7_34
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