  Interactive simulation of ultrasound imaging greatly facilitates sonography
training. Although ray-tracing based methods have shown promising results,
obtaining realistic images requires substantial modeling effort and manual
parameter tuning. In addition, current techniques still result in a significant
appearance gap between simulated images and real clinical scans. In this work
we introduce a novel image translation framework to bridge this appearance gap,
while preserving the anatomical layout of the simulated scenes. We achieve this
goal by leveraging both simulated images with semantic segmentations and
unpaired in-vivo ultrasound scans. Our framework is based on recent contrastive
unpaired translation techniques and we propose a regularization approach by
learning an auxiliary segmentation-to-real image translation task, which
encourages the disentanglement of content and style. In addition, we extend the
generator to be class-conditional, which enables the incorporation of
additional losses, in particular a cyclic consistency loss, to further improve
the translation quality. Qualitative and quantitative comparisons against
state-of-the-art unpaired translation methods demonstrate the superiority of
our proposed framework.
