Improved Segmentation by Adversarial U-Net

Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U-Net that consists of an encoder-decoder based architecture. Many variations exist to the U-Net architecture. In this work, we present a new training procedure that combines U-Net with an adversarial training we refer to as Adversarial U-Net. We show that Adversarial U-Net outperformes the conventional U-Net in three versatile domains that differ in the acquisition method as well as the physical characteristics and yields smooth and improved segmentation maps.