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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