  Anonymization and data sharing are crucial for privacy protection and
acquisition of large datasets for medical image analysis. This is a big
challenge, especially for neuroimaging. Here, the brain's unique structure
allows for re-identification and thus requires non-conventional anonymization.
Generative adversarial networks (GANs) have the potential to provide anonymous
images while preserving predictive properties. Analyzing brain vessel
segmentation, we trained 3 GANs on time-of-flight (TOF) magnetic resonance
angiography (MRA) patches for image-label generation: 1) Deep convolutional
GAN, 2) Wasserstein-GAN with gradient penalty (WGAN-GP) and 3) WGAN-GP with
spectral normalization (WGAN-GP-SN). The generated image-labels from each GAN
were used to train a U-net for segmentation and tested on real data. Moreover,
we applied our synthetic patches using transfer learning on a second dataset.
For an increasing number of up to 15 patients we evaluated the model
performance on real data with and without pre-training. The performance for all
models was assessed by the Dice Similarity Coefficient (DSC) and the 95th
percentile of the Hausdorff Distance (95HD). Comparing the 3 GANs, the U-net
trained on synthetic data generated by the WGAN-GP-SN showed the highest
performance to predict vessels (DSC/95HD 0.82/28.97) benchmarked by the U-net
trained on real data (0.89/26.61). The transfer learning approach showed
superior performance for the same GAN compared to no pre-training, especially
for one patient only (0.91/25.68 vs. 0.85/27.36). In this work, synthetic
image-label pairs retained generalizable information and showed good
performance for vessel segmentation. Besides, we showed that synthetic patches
can be used in a transfer learning approach with independent data. This paves
the way to overcome the challenges of scarce data and anonymization in medical
imaging.
