  Deep Neural Networks (DNNs) suffer from the performance degradation when
image appearance shift occurs, especially in ultrasound (US) image
segmentation. In this paper, we propose a novel and intuitive framework to
remove the appearance shift, and hence improve the generalization ability of
DNNs. Our work has three highlights. First, we follow the spirit of universal
style transfer to remove appearance shifts, which was not explored before for
US images. Without sacrificing image structure details, it enables the
arbitrary style-content transfer. Second, accelerated with Adaptive Instance
Normalization block, our framework achieved real-time speed required in the
clinical US scanning. Third, an efficient and effective style image selection
strategy is proposed to ensure the target-style US image and testing content US
image properly match each other. Experiments on two large US datasets
demonstrate that our methods are superior to state-of-the-art methods on making
DNNs robust against various appearance shifts.
