Unsupervised X-ray image segmentation with task driven generative adversarial networks

Semantic parsing of anatomical structures in X-ray images is a critical task in many clinical applications. Modern methods leverage deep convolutional networks, and generally require a large amount of labeled data for model training. However, obtaining accurate pixel-wise labels on X-ray images is very challenging due to the appearance of anatomy overlaps and complex texture patterns. In comparison, labeled CT data are more accessible since...