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Abstract #1762

sim2real: Cardiac MR image simulation-to-real translation via unsupervised GANs

Sina Amirrajab1, Yasmina Al Khalil1, Cristian Lorenz2, Jürgen Weese2, Josien Pluim1, and Marcel Breeuwer1,3
1Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, Netherlands, 2Philips Research Laboratories, Hamburg, Germany, 3MR R&D - Clinical Science, Philips Healthcare, Best, Netherlands


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.

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