Kidneys are most easily segmented by convolutional neural networks (CNN) on contrast enhanced CT (CECT) images, but their segmentation accuracy may be reduced when only non-contrast CT (NCCT) images are available. The purpose of this work was to investigate the improvement in segmentation accuracy when implementing a generative adversarial network (GAN) to create virtual contrast enhanced (vCECT) images from non-contrast inputs. A 2D cycleGAN model, incorporating an additional idempotent loss function to restrict the GAN from making unnecessary modifications to data already in the translated domain, was trained to generate virtual contrast enhanced images on 286 paired non-contrast and contrast enhanced inputs. A 3D CNN trained on contrast enhanced images was applied to segment the kidneys in a test set of 20 paired non-contrast and contrast enhanced images. The non-contrast images were converted to virtual contrast enhanced images, then kidneys in both image conditions were segmented by the CNN. Segmentation results were compared to analyst annotations on non-contrast images visually and by Dice Coefficient (DC). Segmentation on virtual contrast enhanced images were more complete with fewer extraneous detections compared to non-contrast images in 16/20 cases. Mean(±SD) DC was 0.88(±0.80), 0.90(±0.03), and 0.95(±0.05) for non-contrast, virtual contrast enhanced, and real contrast enhanced, respectively. Virtual contrast enhancement visually improved segmentation quality, poor performing cases had their performance improved resulting in an overall reduction in DC variation, and the minimum DC increased from 0.65 to 0.85. This work provides preliminary results demonstrating the potential effectiveness of using a GAN for virtual contrast enhancement to improve CNN-based kidney segmentation on non-contrast images.