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.
Originalsprache | Englisch |
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Titel | Bildverarbeitung für die Medizin 2019 |
Seitenumfang | 6 |
Herausgeber (Verlag) | Springer Verlag |
Erscheinungsdatum | 01.01.2019 |
Seiten | 74-79 |
ISBN (Print) | 978-3-658-25325-7 |
ISBN (elektronisch) | 978-3-658-25326-4 |
DOIs | |
Publikationsstatus | Veröffentlicht - 01.01.2019 |
Veranstaltung | Workshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Deutschland Dauer: 17.03.2019 → 19.03.2019 Konferenznummer: 224899 |