  Adversarial learning has been proven to be effective for capturing long-range
and high-level label consistencies in semantic segmentation. Unique to medical
imaging, capturing 3D semantics in an effective yet computationally efficient
way remains an open problem. In this study, we address this computational
burden by proposing a novel projective adversarial network, called PAN, which
incorporates high-level 3D information through 2D projections. Furthermore, we
introduce an attention module into our framework that helps for a selective
integration of global information directly from our segmentor to our
adversarial network. For the clinical application we chose pancreas
segmentation from CT scans. Our proposed framework achieved state-of-the-art
performance without adding to the complexity of the segmentor.
