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.
CITATION STYLE
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 Informatik aktuell (pp. 74–79). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-658-25326-4_19
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