  Ischemic strokes are often caused by large vessel occlusions (LVOs), which
can be visualized and diagnosed with Computed Tomography Angiography scans. As
time is brain, a fast, accurate and automated diagnosis of these scans is
desirable. Human readers compare the left and right hemispheres in their
assessment of strokes. A large training data set is required for a standard
deep learning-based model to learn this strategy from data. As labeled medical
data in this field is rare, other approaches need to be developed. To both
include the prior knowledge of side comparison and increase the amount of
training data, we propose an augmentation method that generates artificial
training samples by recombining vessel tree segmentations of the hemispheres or
hemisphere subregions from different patients. The subregions cover vessels
commonly affected by LVOs, namely the internal carotid artery (ICA) and middle
cerebral artery (MCA). In line with the augmentation scheme, we use a
3D-DenseNet fed with task-specific input, fostering a side-by-side comparison
between the hemispheres. Furthermore, we propose an extension of that
architecture to process the individual hemisphere subregions. All
configurations predict the presence of an LVO, its side, and the affected
subregion. We show the effect of recombination as an augmentation strategy in a
5-fold cross validated ablation study. We enhanced the AUC for patient-wise
classification regarding the presence of an LVO of all investigated
architectures. For one variant, the proposed method improved the AUC from 0.73
without augmentation to 0.89. The best configuration detects LVOs with an AUC
of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while
accurately predicting the affected side.
