Abstract: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 restr… Show more
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