Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks

[Paper]

Abstract

Microscopic images from different modality can provide more complete experimental information. In practice, biological and physical limitations may prohibit the acquisition of enough microscopic images at a given observation period. Image synthesis is one promising solution. However, most existing data synthesis methods only translate the image from a source domain to a target domain without strong geometric correlations. To address this issue, we propose a novel model to synthesize diversified microscopic images from multi-sources with different geometric features. The application of our model to a 3D live time-lapse embryonic images of C. elegans presents favorable results. To the best of our knowledge, it is the first effort to synthesize microscopic images with strong underlie geometric correlations from multi-source domains that of entirely separated spatial features.

Citation

@inproceedings{zhuang2020geometrically,
title={Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks},
author={Zhuang, Jun and Wang, Dali},
journal={arXiv preprint arXiv:2010.13308},
year={2020}
}