Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.
CITATION STYLE
Manvel, A., Vladimir, K., Alexander, T., & Dmitry, U. (2019). Radiologist-Level Stroke Classification on Non-contrast CT Scans with Deep U-Net. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11766 LNCS, pp. 820–828). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32248-9_91
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