  Endovascular intervention training is increasingly being conducted in virtual
simulators. However, transferring the experience from endovascular simulators
to the real world remains an open problem. The key challenge is the virtual
environments are usually not realistically simulated, especially the simulation
images. In this paper, we propose a new method to translate simulation images
from an endovascular simulator to X-ray images. Previous image-to-image
translation methods often focus on visual effects and neglect structure
information, which is critical for medical images. To address this gap, we
propose a new method that utilizes multi-scale semantic matching. We apply
self-domain semantic matching to ensure that the input image and the generated
image have the same positional semantic relationships. We further apply
cross-domain matching to eliminate the effects of different styles. The
intensive experiment shows that our method generates realistic X-ray images and
outperforms other state-of-the-art approaches by a large margin. We also
collect a new large-scale dataset to serve as the new benchmark for this task.
Our source code and dataset will be made publicly available.

Image and Video Processing;Computer Vision and Pattern Recognition