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.

翻译：超声成像的交互式模拟极大地促进了声学培训。虽然以射线追踪为基础的方法已经显示出有希望的结果,但获取现实的图像需要大量的建模努力和人工参数调整。此外,目前的技术仍然导致模拟图像和真正的临床扫描之间的巨大外观差距。在这项工作中,我们引入了一个新的图像翻译框架以弥合这一外观差距,同时保留模拟场景的解剖版面。我们通过利用模拟图像与语义分解和未受静音超声扫描的模拟图像来实现这一目标。我们的框架以最近的对比性非光化翻译技术为基础,我们提出一种正规化方法,学习辅助性分解到真实图像的翻译任务,这鼓励了内容和风格的分解。此外,我们扩大了生成器的等级性条件,从而能够纳入更多的损失,特别是周期一致性损失,从而进一步提高翻译质量。我们提出的框架的定性和定量比较表明我们拟议框架的优越性。