URO-GAN: An untrustworthy region optimization approach for adipose tissue segmentation based on adversarial learning

摘要

Automatic segmentation of adipose tissue from CT images is an essential module of medical assistant diagnosis. A large scale of abdominal cross-section CT images can be used to segment subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) with deep learning method. However, the CT images still need to be professionally and accurately annotated to improve the segmentation quality. The paper proposes a semi-supervised segmentation network based on adversarial learning. The model is called URO-GAN and consists of two paths used to segment SAT and VAT, respectively. An SAT-to-VAT transmission mechanism is set up between these two paths, where several inverse-SAT excitation blocks are set to help the SAT segmentation network guide the VAT segmentation network. An untrustworthy region optimization mechanism is proposed to improve the segmentation quality and keep the adversarial learning stable. With the confidence map output from the discriminator network, an optimizer network is used to fix the error in the masks predicted by the segmentation network. The URO-GAN achieves good results by training with 84 annotated images and 3969 unannotated images. Experimental results demonstrate the effectiveness of our approach on the segmentation of adipose tissue in medical images.