Synthesizing retinal and neuronal images with generative adversarial nets

Published date : 04 Jul 2018

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.

type
Journal Paper
journal
Medical Image Analysis, 2018 Jul 4;49:14-26. doi: 10.1016/j.media.2018.07.001
pubmed
30007254
Url
https://www.ncbi.nlm.nih.gov/pubmed/30007254
Impact Factor
5.356
Date of acceptance