Title
SkrGAN: Sketching-Rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis.
Abstract
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to guide the medical image generation. In our SkrGAN, a sketch guidance module is utilized to generate a high quality structural sketch from random noise, then a color render mapping is used to embed the sketch-based representations and resemble the background appearances. Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In addition, we also show that the performances of medical image segmentation method has been improved by using our synthesized images as data augmentation.
Year
DOI
Venue
2019
10.1007/978-3-030-32251-9_85
Lecture Notes in Computer Science
Keywords
DocType
Volume
Deep learning,Generative Adversarial Networks,Medical image synthesis
Conference
11767
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Tianyang Zhang1638.35
Huazhu Fu2123565.07
Yitian Zhao324633.15
Jun Cheng421420.65
Mengjie Guo500.34
Zaiwang Gu6855.88
Bing Yang700.34
Yuting Xiao862.38
Shenghua Gao9160766.89
Jiang Liu1033534.30