  For a unified analysis of medical images from different modalities, data
harmonization using image-to-image (I2I) translation is desired. We study this
problem employing an optical coherence tomography (OCT) data set of
Spectralis-OCT and Home-OCT images. I2I translation is challenging because the
images are unpaired, and a bijective mapping does not exist due to the
information discrepancy between both domains. This problem has been addressed
by the Contrastive Learning for Unpaired I2I Translation (CUT) approach, but it
reduces semantic consistency. To restore the semantic consistency, we support
the style decoder using an additional segmentation decoder. Our approach
increases the similarity between the style-translated images and the target
distribution. Importantly, we improve the segmentation of biomarkers in
Home-OCT images in an unsupervised domain adaptation scenario. Our data
harmonization approach provides potential for the monitoring of diseases, e.g.,
age related macular disease, using different OCT devices.
