Meeting Banner
Abstract #1837

Semantic CMR Synthesis: Generating Coherent Short- and Long-Axis Images with Corresponding Multi-Class Anatomical Labels

Thomas Joyce1, Nico Schulthess1, Gloria Wolkerstorfer1, Stefano Buoso1, and Sebastian Kozerke1
1University and ETH Zurich, Zurich, Switzerland


We propose to use a combination of the StyleGAN2, ADA and DatasetGAN methods to produce synthetic short- and long-axis view cardiac magnetic resonance (CMR) images accompanied with corresponding 11-class tissue masks. The image generator networks are trained on datasets of approximately 1850 and 5000 unlabelled images, for short- and long-axis images respectively. The segmentation networks are trained on only 30 manually annotated synthetic images in total. We further demonstrate a proof-of-concept method for generating coherent long- and short-axis images of the same synthetic patient.

This abstract and the presentation materials are available to members only; a login is required.

Join Here