The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms.

Here, only the generated synthetic CT image data are provided. For generating body models as basis for synthetic CT generation you need to license the XCAT phantom (https://otc.duke.edu/industry-investors/available-technologies/xcat/).