Automatic Detection and Segmentation of the Acute Vessel Thrombus in Cerebral CT
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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- 1.Mackay J, Mensah G, eds. The atlas of heart disease and stroke. WHO. 2004; p. 18–19.Google Scholar
- 3.Winzeck S, Hakim A, McKinley R, et al. ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front Neurol. 2018;9:679.Google Scholar
- 6.Lisowska A, O’Neil A, Dilys V, et al. Context-aware convolutional neural networks for stroke sign detection in non-contrast CT scans. Med Image Underst Anal. 2017; p. 494–505.Google Scholar
- 8.Sakamoto M, Nakano H, Zhao K, et al. Multi-stage neural networks with singlesided classifiers for false positive reduction and its evaluation using lung X-ray CT images. Proc ICIAP. 2017; p. 370–379.Google Scholar
- 9.Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Proc MICCAI. 2015; p. 234–241.Google Scholar