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Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection

: Ivantsits, M.; Kuhnigk, J.-M.; Huellebrand, M.; 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 ()

Subarachnoid hemorrhage, commonly caused by the rupture of cerebral aneurysms, is a life-threatening condition with high mortality and morbidity. With a death rate of roughly 40%, it is highly desirable to detect aneurysms early and decide about the appropriate rupture prevention strategy. Rotational X-ray angiography is a non-invasive imaging modality and enables diagnostics to detect cerebral aneurysms at an early stage.
We propose a variation of the 3D U-Net architecture for the detection and localization of these cerebral aneurysms. This model is enhanced with a knowledge-based postprocessing strategy to minimize the false-positive detections per case. Our suggested method shows similar sensitivity statistics compared to state-of-the-art solutions, with a drastically reduced false-positive rate per patient. The described solution is almost entirely accurate on structures larger than 5 mm in diameter but shows difficulties with smaller aneurysms. We show an F2-score of 0.84 and a false-positive rate of 0.41 on a private test set.