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.

本论文中，我们提出了一种利用多尺度语义匹配的图像翻译方法，旨在将内镜模拟器中的图像翻译成X光图像。通过进行自领域和交叉域匹配，我们解决了图像翻译中存在的结构信息丢失等问题，实现了高度逼真的 X 光图像生成效果，并超过其他最先进的方法。

通过多尺度语义匹配将仿真图像转换为X射线图像