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Large annotated datasets are required to train segmentation networks. Inmedical imaging, it is often difficult, time consuming and expensive to createsuch datasets, and it may also be difficult to share these datasets with otherresearchers. Different AI models can today generate very realistic syntheticimages, which can potentially be openly shared as they do not belong tospecific persons. However, recent work has shown that using synthetic imagesfor training deep networks often leads to worse performance compared to usingreal images. Here we demonstrate that using synthetic images and annotationsfrom an ensemble of 20 GANs, instead of from a single GAN, increases the Dicescore on real test images with 4.7 % to 14.0 % on specific classes.

Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?