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Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning

: Ivantsits, M.; Huellebrand, M.; Kelle, S.; Kuehne, T.; Hennemuth, A.


Hennemuth, A.:
Cerebral Aneurysm Detection and Analysis. First Challenge, CADA 2020. Proceedings : Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020
Cham: Springer Nature, 2021 (Lecture Notes in Computer Science 12643)
ISBN: 978-3-030-72861-8 (Print)
ISBN: 978-3-030-72862-5 (Online)
ISBN: 978-3-030-72863-2
Cerebral Aneurysm Detection and Analysis Challenge (CADA) <1, 2020, Online>
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <23, 2020, Online>
Conference Paper
Fraunhofer MEVIS ()

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.