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  Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks

Kossen, T., Subramaniam, P., Madai, V. I., Hennemuth, A., Hildebrand, K., Hilbert, A., et al. (2021). Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks. Computers in Biology and Medicine, 131: 104254. doi:10.1016/j.compbiomed.2021.104254.

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 Creators:
Kossen, Tabea1, 2, Author
Subramaniam, Pooja1, 3, Author
Madai, Vince I.1, 4, Author
Hennemuth, Anja2, 5, 6, Author
Hildebrand, Kristian7, Author
Hilbert, Adam1, Author
Sobesky, Jan8, 9, Author
Livne, Michelle1, Author
Galinovic, Ivana9, Author
Khalil, Ahmed9, 10, 11, 12, Author              
Fiebach, Jochen B.9, Author
Frey, Dietmar1, Author
Affiliations:
1Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité University Medicine Berlin, Germany, ou_persistent22              
2Department of Computer Engineering and Microelectronics, TU Berlin, Germany, ou_persistent22              
3Faculty of Electrical Engineering and Computer Science, TU Berlin, Germany, ou_persistent22              
4School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, University of Birmingham, United Kingdom, ou_persistent22              
5Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité University Medicine Berlin, Germany, ou_persistent22              
6Fraunhofer MEVIS, Bremen, Germany, ou_persistent22              
7Department VI Computer Science and Media, Beuth University of Applied Sciences, Berlin, Germany, ou_persistent22              
8Johanna-Etienne-Hospital, Neuss, Germany, ou_persistent22              
9Center for Stroke Research, Charité University Medicine Berlin, Germany, ou_persistent22              
10Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
11MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
12Berlin Institute of Health (BIH), Germany, ou_persistent22              

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Free keywords: Anonymization; Generative adversarial networks; Image segmentation
 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 0.85/30.00) benchmarked by the U-net trained on real data (0.89/26.57). The transfer learning approach showed superior performance for the same GAN compared to no pre-training, especially for one patient only (0.91/24.66 vs. 0.84/27.36). 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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Language(s): eng - English
 Dates: 2021-01-272020-11-172021-02-032021-02-152021-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.compbiomed.2021.104254
Other: epub 2021
PMID: 33618105
 Degree: -

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Title: Computers in Biology and Medicine
  Other : Comput. Biol. Med.
Source Genre: Journal
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Publ. Info: New York, NY : Pergamon Press
Pages: - Volume / Issue: 131 Sequence Number: 104254 Start / End Page: - Identifier: ISSN: 0010-4825
CoNE: https://pure.mpg.de/cone/journals/resource/954925392327