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Abstract #3523

Preserving Privacy While Maintaining Consistent Postprocessing Results: Fast and Effective Anonymous Refacing using a 3D cGAN

Nataliia Molchanova1,2, Bénédicte Maréchal1,2,3, Jean-Philippe Thiran2,3, Tobias Kober1,2,3, Jonas Richiardi3, and Till Huelnhagen1,2,3
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 3Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland


We propose a novel refacing technique employing a 3D conditional generative adversarial network to allow protecting subject privacy while maintaining consistent post-processing results. We evaluate the method compared to current refacing techniques using brain morphometry as an example. Results show that the proposed method compromises brain morphometry results to a lesser extent than existing methods while showing lower similarity of the final image to the original one, hence suggesting an improved privacy protection. We conclude that the proposed method represents a fast and viable alternative for image data de-identification compared to currently existing methods.

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