Zusammenfassung
Rupture risk analysis of intracranial aneurysms is important for treatment decisions. Morphological parameters like size, diameter or aspect ratio are used to capture the relevant aspects of the aneurysm shape and predict the rupture of intracranial aneurysms. Automatic calculation of these parameters is cumbersome, whereas manual measurements are time-consuming, error-prone and subject to inter-observer variance. Instead of classification based on morphological parameters, here, deep learning on aneurysm surface meshes is used to classify 3D surface meshes of intracranial aneurysm into ruptured and unruptured. We compared several deep learning approaches on surfaces meshes and point clouds showing patient-specific aneurysm geometries. Using 150 aneurysms for training and 40 for testing, a test accuracy of 82,5% was achieved.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Niemann, A., Preim, B., Beuing, O., Saalfeld, S. (2022). Predicting Aneurysm Rupture with Deep Learning on 3D Models. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_65
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DOI: https://doi.org/10.1007/978-3-658-36932-3_65
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