Synthesizing Filamentary Structured Images with GANs

2017/06/07 by He Zhao, Huiqi Li, Zhao, He +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Image Processing Techniques #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #cs.CV

paper · pdf · doi:10.48550/arxiv.1706.02185

arxiv created 2017/06/07 · openalex publication_date 2017/06/07 · arxiv updated 2017/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper aims at synthesizing filamentary structured images such as retinal fundus images and neuronal images, as follows: Given a ground-truth, to generate multiple realistic looking phantoms. A ground-truth could be a binary segmentation map containing the filamentary structured morphology, while the synthesized output image is of the same size as the ground-truth and has similar visual appearance to what have been presented in the training set. Our approach is inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer. In particular, it is dedicated to our problem context with the following properties: Rather than large-scale dataset, it works well in the presence of as few as 10 training examples, which is common in medical image analysis; It is capable of synthesizing diverse images from the same ground-truth; Last and importantly, the synthetic images produced by our approach are demonstrated to be useful in boosting image analysis performance. Empirical examination over various benchmarks of fundus and neuronal images demonstrate the advantages of the proposed approach.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Auto-Encoding Variational Bayes
• Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Related

• GANs for Medical Image Synthesis: An Empirical Study
• Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data
• High-resolution medical image synthesis using progressively grown generative adversarial networks
• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
• SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis