Improved segmentation by adversarial u-net
David Sriker, Dana Cohen, Noa Cahan, Hayit Greenspan · 2021
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.