Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks - Kossen, Tabea; Hirzel, Manuel A.; Madai, Vince I.; Boenisch, Franziska; Hennemuth, Anja; Hildebrand, Kristian; Pokutta, Sebastian; Sharma, Kartikey; Hilbert, Adam; Sobesky, Jan; Galinovic, Ivana; Khalil, Ahmed A.; et al. (Frontiers in Artificial Intelligence, 2022)
Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient’s privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight (TOF) Magnetic Resonance Angiography (MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the similarity of the generated images to the real images was evaluated using the Fréchet Inception Distance (FID). During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter epsilon. With stricter privacy guarantees, the segmentation performance as well as the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for epsilon = 7.4 compared to 0.84 for epsilon = infinity in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set respectively). We identified a threshold of epsilon < 5 for which the performance (DSC<0.61) became unstable and not usable. Our synthesized, labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.