Abstract
Intracranial aneurysms frequently cause subarachnoid hemorrhage—a life-threatening condition with a high mortality and morbidity rate. State-of-the-art methods combine demographic, clinical, morphological, and computational fluid dynamics parameters.
We propose a method combining morphological radiomics features, gray-level radiomics features, and a novel aneurysm site location encoding via directed graphs on the vessel tree. Some of the gray-level features seem to be good proxies for blood flow within the vessel and the aneurysms. Furthermore, our proposed method shows improved F2-scores and accuracy across various models fed with the aneurysm site encoding. A K-nearest neighbors method shows the best results during our model selection with an F2-score of 0.7 and an accuracy of 0.73 on the relatively small private test set with 22 individuals and 30 aneurysms.
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Ivantsits, M., Huellebrand, M., Kelle, S., Kuehne, T., Hennemuth, A. (2021). Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning. In: Hennemuth, A., Goubergrits, L., Ivantsits, M., Kuhnigk, JM. (eds) Cerebral Aneurysm Detection and Analysis. CADA 2020. Lecture Notes in Computer Science(), vol 12643. Springer, Cham. https://doi.org/10.1007/978-3-030-72862-5_10
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DOI: https://doi.org/10.1007/978-3-030-72862-5_10
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