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.