  Computed Tomography Angiography is a key modality providing insights into the
cerebrovascular vessel tree that are crucial for the diagnosis and treatment of
ischemic strokes, in particular in cases of large vessel occlusions (LVO).
Thus, the clinical workflow greatly benefits from an automated detection of
patients suffering from LVOs. This work uses convolutional neural networks for
case-level classification trained with elastic deformation of the vessel tree
segmentation masks to artificially augment training data. Using only masks as
the input to our model uniquely allows us to apply such deformations much more
aggressively than one could with conventional image volumes while retaining
sample realism. The neural network classifies the presence of an LVO and the
affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate
that the use of the suggested augmentation enables us to train robust models
even from few data sets. Training the EfficientNetB1 architecture on 100 data
sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85
from a baseline value of 0.56 using no augmentation. The best performance was
achieved using a 3D-DenseNet yielding an AUC of 0.87. The augmentation had
positive impact in classification of the affected hemisphere as well, where the
3D-DenseNet reached an AUC of 0.93 on both sides.
