  Deep learning based disease detection and segmentation algorithms promise to
improve many clinical processes. However, such algorithms require vast amounts
of annotated training data, which are typically not available in the medical
context due to data privacy, legal obstructions, and non-uniform data
acquisition protocols. Synthetic databases with annotated pathologies could
provide the required amounts of training data. We demonstrate with the example
of ischemic stroke that an improvement in lesion segmentation is feasible using
deep learning based augmentation. To this end, we train different
image-to-image translation models to synthesize magnetic resonance images of
brain volumes with and without stroke lesions from semantic segmentation maps.
In addition, we train a generative adversarial network to generate synthetic
lesion masks. Subsequently, we combine these two components to build a large
database of synthetic stroke images. The performance of the various models is
evaluated using a U-Net which is trained to segment stroke lesions on a
clinical test set. We report a Dice score of $\mathbf{72.8}$%
[$\mathbf{70.8\pm1.0}$%] for the model with the best performance, which
outperforms the model trained on the clinical images alone $\mathbf{67.3}$%
[$\mathbf{63.2\pm1.9}$%], and is close to the human inter-reader Dice score of
$\mathbf{76.9}$%. Moreover, we show that for a small database of only 10 or 50
clinical cases, synthetic data augmentation yields significant improvement
compared to a setting where no synthetic data is used. To the best of our
knowledge, this presents the first comparative analysis of synthetic data
augmentation based on image-to-image translation, and first application to
ischemic stroke.
