  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. Herein we
introduce a novel content-preserving image translation framework (ConPres) to
bridge this appearance gap, while maintaining the simulated anatomical layout.
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.
