  Image-to-image translation is a new field in computer vision with multiple
potential applications in the medical domain. However, for supervised image
translation frameworks, co-registered datasets, paired in a pixel-wise sense,
are required. This is often difficult to acquire in realistic medical
scenarios. On the other hand, unsupervised translation frameworks often result
in blurred translated images with unrealistic details. In this work, we propose
a new unsupervised translation framework which is titled Cycle-MedGAN. The
proposed framework utilizes new non-adversarial cycle losses which direct the
framework to minimize the textural and perceptual discrepancies in the
translated images. Qualitative and quantitative comparisons against other
unsupervised translation approaches demonstrate the performance of the proposed
framework for PET-CT translation and MR motion correction.
