Improved segmentation by adversarial U-Net

David Sriker; Dana Cohen; Noa Cahan; Hayit Greenspan

doi:10.1117/12.2582130

15 February 2021 Improved segmentation by adversarial U-Net

David Sriker, Dana Cohen, Noa Cahan, Hayit Greenspan

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159719 (2021) https://doi.org/10.1117/12.2582130
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

David Sriker, Dana Cohen, Noa Cahan, and Hayit Greenspan "Improved segmentation by adversarial U-Net", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159719 (15 February 2021); https://doi.org/10.1117/12.2582130

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