sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
This work addresses the problem of limited realism in simulated medical images for researchers and practitioners in medical imaging, though it is incremental as it builds on existing GAN-based translation methods.
The paper tackles the realism gap in simulated cardiac MR images by proposing a sim2real translation network, which improves image realism and boosts segmentation algorithm performance in usability experiments.
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.