Synthesizing retinal and neuronal images with generative adversarial nets

Synthesizing retinal and neuronal images with generative adversarial nets
Title:
Synthesizing retinal and neuronal images with generative adversarial nets
Other Titles:
Medical Image Analysis
Publication Date:
04 July 2018
Citation:
He Zhao, Huiqi Li, Sebastian Maurer-Stroh, Li Cheng, Synthesizing retinal and neuronal images with generative adversarial nets, Medical Image Analysis, Volume 49, 2018, Pages 14-26, ISSN 1361-8415, https://doi.org/10.1016/j.media.2018.07.001.
Abstract:
This paper aims at synthesizing multiple realistic-looking retinal (or neuronal) images from an unseen tubular structured annotation that contains the binary vessel (or neuronal) morphology. The generated phantoms are expected to preserve the same tubular structure, and resemble the visual appearance of the training images. Inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer, our approach enjoys several advantages. It works well with a small training set with as few as 10 training examples, which is a common scenario in medical image analysis. Besides, it is capable of synthesizing diverse images from the same tubular structured annotation. Extensive experimental evaluations on various retinal fundus and neuronal imaging applications demonstrate the merits of the proposed approach.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
The project is partially supported by A*STAR JCO grants.
Description:
ISSN:
1361-8415
1361-8423
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