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Ultrasound (US) is widely accepted in clinic for anatomical structureinspection. However, lacking in resources to practice US scan, novices oftenstruggle 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 cansolve the problems. The task is challenging and previous methods can onlypartially complete it. In this paper, we devise a new framework for US imagesynthesis. Particularly, we firstly adopt a sketch generative adversarialnetworks (Sgan) to introduce background sketch upon object mask in aconditioned generative adversarial network. With enriched sketch cues, Sgan cangenerate realistic US images with editable and fine-grained structure details.Although effective, Sgan is hard to generate high resolution US images. Toachieve this, we further implant the Sgan into a progressive growing scheme(PGSgan). By smoothly growing both generator and discriminator, PGSgan cangradually synthesize US images from low to high resolution. By synthesizingovary and follicle US images, our extensive perceptual evaluation, user studyand segmentation results prove the promising efficacy and efficiency of theproposed PGSgan.

Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs