Abstract
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.
Original language | English |
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Title of host publication | Bildverarbeitung für die Medizin 2019 |
Number of pages | 6 |
Publisher | Springer Verlag |
Publication date | 01.01.2019 |
Pages | 74-79 |
ISBN (Print) | 978-3-658-25325-7 |
ISBN (Electronic) | 978-3-658-25326-4 |
DOIs | |
Publication status | Published - 01.01.2019 |
Event | Workshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Germany Duration: 17.03.2019 → 19.03.2019 Conference number: 224899 |