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Stroke is one of the leading causes of death worldwide. Medical imaging techniques such as magnetic resonance imaging offer the possibility to extract essential individual information about the disease that allowed for better patient care in the past decades. The advancement in computational power and increase in data availability has led to the rise of Deep Learning (DL) models, also for medical images. While DL methods have shown promising results in automating the processing of medical images, a major challenge remains data availability, as acquiring medical data is expensive and time-consuming. Additionally, medical images often need to be annotated by medical experts to be useful for DL models. A solution to this would be data sharing, but this is often hindered by privacy regulations. To sustain the patient’s privacy and still allow for data sharing, synthesizing artificial images could be an encouraging remedy. For this, Generative Adversarial Networks (GANs) are gaining much attention. GANs usually consist of two competing neural networks with one network, the generator, synthesizing data samples. In contrast, the other network, the discriminator, judges how realistic the sample looks and provides feedback to both networks. In this thesis, we generate synthetic images using different GANs for two purposes in the stroke domain: sharing of labeled images and automated image processing for treatment planning. In the first part, we synthesize medical image patches for segmentation along with their respective segmentation labels. We evaluate our synthetic data by training a segmentation network on synthetic data and testing their …