论文标题
SIM2REAL：心脏MR图像模拟对真实的翻译通过无监督的gan
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
论文作者
论文摘要
对于基于MR物理学的基于MR物理学的仿真，对于虚拟心脏MR图像的数据库而言，人们引起了极大的兴趣，以开发深度学习分析网络。但是，这种数据库的使用受到限制或由于现实差距，缺失纹理以及模拟图像的简化外观而显示出次优性能。在这项工作中，我们1）在虚拟XCAT受试者上提供不同的解剖学模拟，以及2）提出SIM2Real Translation网络以改善图像现实主义。我们的可用性实验表明，SIM2REAL数据具有增强训练数据并提高分割算法的性能的良好潜力。
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks. However, the employment of such a database is limited or shows suboptimal performance due to the realism gap, missing textures, and the simplified appearance of simulated images. In this work we 1) provide image simulation on virtual XCAT subjects with varying anatomies, and 2) propose sim2real translation network to improve image realism. Our usability experiments suggest that sim2real data exhibits a good potential to augment training data and boost the performance of a segmentation algorithm.
