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More than 13 million people suffer from ischemic cerebral stroke worldwideeach year. Thrombolytic treatment can reduce brain damage but has a narrowtreatment window. Computed Tomography Perfusion imaging is a commonly usedprimary assessment tool for stroke patients, and typically the radiologistswill evaluate resulting parametric maps to estimate the affected areas, deadtissue (core), and the surrounding tissue at risk (penumbra), to decide furthertreatments. Different work has been reported, suggesting thresholds, andsemi-automated methods, and in later years deep neural networks, for segmentinginfarction areas based on the parametric maps. However, there is no consensusin terms of which thresholds to use, or how to combine the information from theparametric maps, and the presented methods all have limitations in terms ofboth accuracy and reproducibility.  We propose a fully automated convolutional neural network based segmentationmethod that uses the full four-dimensional computed tomography perfusiondataset as input, rather than the pre-filtered parametric maps. The suggestednetwork is tested on an available dataset as a proof-of-concept, with veryencouraging results. Cross-validated results show averaged Dice score of 0.78and 0.53, and an area under the receiver operating characteristic curve of 0.97and 0.94 for penumbra and core respectively

CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging