Zusammenfassung
Intervention time plays a very important role for stroke outcome and affects different therapy paths. Automatic detection of an ischemic condition during emergency imaging could draw the attention of a radiologist directly to the thrombotic clot. Considering an appropriate early treatment, the immediate automatic detection of a clot could lead to a better patient outcome by reducing time-to-treatment. We present a two-stage neural network to automatically segment and classify clots in the MCA+ICA region for a fast pre-selection of positive cases to support patient triage and treatment planning. Our automatic method achieves an area under the receiver operating curve (AUROC) of 0:99 for the correct positive/negative classification on unseen test data.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Lucas, C., Schöttler, J.J., Kemmling, A., Aulmann, L.F., Heinrich, M.P. (2019). Automatic Detection and Segmentation of the Acute Vessel Thrombus in Cerebral CT. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_19
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DOI: https://doi.org/10.1007/978-3-658-25326-4_19
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