  Ultrasound (US) imaging is widely used for anatomical structure inspection in
clinical diagnosis. The training of new sonographers and deep learning based
algorithms for US image analysis usually requires a large amount of data.
However, obtaining and labeling large-scale US imaging data are not easy tasks,
especially for diseases with low incidence. Realistic US image synthesis can
alleviate this problem to a great extent. In this paper, we propose a
generative adversarial network (GAN) based image synthesis framework. Our main
contributions include: 1) we present the first work that can synthesize
realistic B-mode US images with high-resolution and customized texture editing
features; 2) to enhance structural details of generated images, we propose to
introduce auxiliary sketch guidance into a conditional GAN. We superpose the
edge sketch onto the object mask and use the composite mask as the network
input; 3) to generate high-resolution US images, we adopt a progressive
training strategy to gradually generate high-resolution images from
low-resolution images. In addition, a feature loss is proposed to minimize the
difference of high-level features between the generated and real images, which
further improves the quality of generated images; 4) the proposed US image
synthesis method is quite universal and can also be generalized to the US
images of other anatomical structures besides the three ones tested in our
study (lung, hip joint, and ovary); 5) extensive experiments on three large US
image datasets are conducted to validate our method. Ablation studies,
customized texture editing, user studies, and segmentation tests demonstrate
promising results of our method in synthesizing realistic US images.
