  Ultrasound imaging is pivotal in various medical diagnoses due to its
non-invasive nature and safety. In clinical practice, the accuracy and
precision of ultrasound image analysis are critical. Recent advancements in
deep learning are showing great capacity of processing medical images. However,
the data hungry nature of deep learning and the shortage of high-quality
ultrasound image training data suppress the development of deep learning based
ultrasound analysis methods. To address these challenges, we introduce an
advanced deep learning model, dubbed S-CycleGAN, which generates high-quality
synthetic ultrasound images from computed tomography (CT) data. This model
incorporates semantic discriminators within a CycleGAN framework to ensure that
critical anatomical details are preserved during the style transfer process.
The synthetic images produced are used to augment training datasets for
semantic segmentation models and robot-assisted ultrasound scanning system
development, enhancing their ability to accurately parse real ultrasound
imagery.
