Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks

• URL: http://arxiv.org/abs/2009.04227v3
• Date: Mon, 16 Nov 2020 17:27:35 GMT
• Title: Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks
• Authors: Tabea Kossen, Pooja Subramaniam, Vince I. Madai, Anja Hennemuth, Kristian Hildebrand, Adam Hilbert, Jan Sobesky, Michelle Livne, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach and Dietmar Frey
• Abstract summary: Generative adversarial networks (GANs) have the potential to provide anonymous images while preserving predictive properties. We trained 3 GANs on time-of-flight (TOF) magnetic resonance angiography (MRA) patches for image-label generation. The generated image-labels from each GAN were used to train a U-net for segmentation and tested on real data.
• Score: 0.9854633436173144
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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