  Ultrasound (US) is widely accepted in clinic for anatomical structure
inspection. However, lacking in resources to practice US scan, novices often
struggle to learn the operation skills. Also, in the deep learning era,
automated US image analysis is limited by the lack of annotated samples.
Efficiently synthesizing realistic, editable and high resolution US images can
solve the problems. The task is challenging and previous methods can only
partially complete it. In this paper, we devise a new framework for US image
synthesis. Particularly, we firstly adopt a sketch generative adversarial
networks (Sgan) to introduce background sketch upon object mask in a
conditioned generative adversarial network. With enriched sketch cues, Sgan can
generate realistic US images with editable and fine-grained structure details.
Although effective, Sgan is hard to generate high resolution US images. To
achieve this, we further implant the Sgan into a progressive growing scheme
(PGSgan). By smoothly growing both generator and discriminator, PGSgan can
gradually synthesize US images from low to high resolution. By synthesizing
ovary and follicle US images, our extensive perceptual evaluation, user study
and segmentation results prove the promising efficacy and efficiency of the
proposed PGSgan.
