  In surgical computer vision applications, obtaining labeled training data is
challenging due to data-privacy concerns and the need for expert annotation.
Unpaired image-to-image translation techniques have been explored to
automatically generate large annotated datasets by translating synthetic images
to the realistic domain. However, preserving the structure and semantic
consistency between the input and translated images presents significant
challenges, mainly when there is a distributional mismatch in the semantic
characteristics of the domains. This study empirically investigates unpaired
image translation methods for generating suitable data in surgical
applications, explicitly focusing on semantic consistency. We extensively
evaluate various state-of-the-art image translation models on two challenging
surgical datasets and downstream semantic segmentation tasks. We find that a
simple combination of structural-similarity loss and contrastive learning
yields the most promising results. Quantitatively, we show that the data
generated with this approach yields higher semantic consistency and can be used
more effectively as training data.The code is available at
https://gitlab.com/nct_tso_public/constructs.
