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Background The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks. Methods CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions. Cerebral hemispheric comparison CTA and non-contrast computed tomography (NCCT) were studied as additional input features. Results All ischemic lesions in the testing data were correctly lateralized, and a high correspondence to manual segmentations was achieved. Patients with a diagnosed stroke had clinically relevant regions labeled infarcted with a 0.93 sensitivity and 0.82 specificity. The highest achieved voxel-wise area under receiver operating characteristic curve was 0.93, and the highest Dice similarity coefficient was 0.61. When cerebral hemispheric comparison was used as an input feature, the algorithm performance improved. Only a slight effect was seen when NCCT was included. Conclusion The results support the hypothesis that an acute ischemic stroke lesion can be detected with 3D convolutional neural network-based software from CTA-SI. Utilizing information from the contralateral hemisphere appears to be beneficial for reducing false positive findings.

Purpose The aim of this study was to investigate if 3D convolutional deep neural network implementation DeepMedic [1] could be applied to computed tomography angiography (CTA) images in acute stroke lesion detection. DeepMedic has previously been successful in lesion segmentation from magnetic resonance images [2] . Methods Preprocessing steps included robust intracranial space segmentation and intensity normalization. Hypoattenuated regions visible in the CTAs were manually delineated by two neuroradiologists and considered as ground truths. Data augmentation was performed with left–right flipped images. Training was done with scans from five infarct patients and subsequent testing with five separate cases. 35 epochs with 15 subepochs were used in the training. Results The initial results were promising as the infarcts in all the test cases could be correctly lateralized. The achieved voxel-wise segmentation performance was moderate (considering small training set) with Sorensen-Dice similarity coefficient 0.52 for the test data. Conclusions The significance of open science and freely available scientific software will only increase in the future. Convolutional neural networks demonstrate promising prospects for medical imaging. DeepMedic can be applied to CT images with proper data preprocessing. In a future study the findings will be verified with a larger cohort. Improving the performance with additional input features will also be investigated.