  Anonymization of medical images is necessary for protecting the identity of
the test subjects, and is therefore an essential step in data sharing. However,
recent developments in deep learning may raise the bar on the amount of
distortion that needs to be applied to guarantee anonymity. To test such
possibilities, we have applied the novel CycleGAN unsupervised image-to-image
translation framework on sagittal slices of T1 MR images, in order to
reconstruct facial features from anonymized data. We applied the CycleGAN
framework on both face-blurred and face-removed images. Our results show that
face blurring may not provide adequate protection against malicious attempts at
identifying the subjects, while face removal provides more robust
anonymization, but is still partially reversible.
