Transferring Microscopy Image Modalities With Conditional Generative Adversarial Networks

Liang Han, Zhaozheng Yin; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 99-107

Abstract


Phase Contrast (PC) and Differential Interference Contrast (DIC) microscopy are two popular noninvasive techniques for monitoring live cells. Each of these two image modalities has its own advantages and disadvantages to visualize specimens. In this paper, we investigate a conditional Generative Adversarial Network (conditional GAN) which contains one generator and two discriminators to transfer microscopy image modalities. Given a training dataset consisting of pairs of images (source and destination) captured on the same set of specimens by DIC and Phase Contrast microscopes, we can train a conditional GAN, and with this well-trained GAN, we can generate the corresponding Phase Contrast image given a new DIC image, vice versa. The preliminary experiments demonstrate that our approach outperforms one state-of-the-arts method, and can provide biologists a computational way to switch between microscopy image modalities.

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[bibtex]
@InProceedings{Han_2017_CVPR_Workshops,
author = {Han, Liang and Yin, Zhaozheng},
title = {Transferring Microscopy Image Modalities With Conditional Generative Adversarial Networks},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}