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.
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