Paper accepted by the prestigious MIDL 2020 conference

4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model

Available on Openreview and arXiv.

We are thrilled to share with you our recent paper accepted for @midl_conference 2020 presenting a novel hybrid controllable image generation method for synthesizing anatomically meaningful 3D+t labelled CMR images. Check out how we efficiently combine a physics-driven anatomical model with a SOTA data-driven generative adversarial network to generate realistic-looking cardiac MR images. As a part of the OpenGTN research, we step towards tackling the limited availability of medical data for deep learning training.

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