Green Fluorescent Protein and Phase-Contrast Image Fusion via Generative Adversarial Networks

• Published in: Computational and mathematical methods in medicine 2019, Vol.2019 (2019), p.1-11
• Main Authors: Peng, Hu, Cheng, Juan, Zhang, Chao, Liu, Yu, Tang, Wei, Chen, Xun
• Format: Article
• Language: English
• Subjects: Algorithms; Cell Biology; Computational Biology; Deep Learning; Green Fluorescent Proteins - metabolism; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Microscopy, Phase-Contrast - methods; Microscopy, Phase-Contrast - statistics & numerical data; Models, Biological
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2019/5450373

In the field of cell and molecular biology, green fluorescent protein (GFP) images provide functional information embodying the molecular distribution of biological cells while phase-contrast images maintain structural information with high resolution. Fusion of GFP and phase-contrast images is of high significance to the study of subcellular localization, protein functional analysis, and genetic expression. This paper proposes a novel algorithm to fuse these two types of biological images via generative adversarial networks (GANs) by carefully taking their own characteristics into account. The fusion problem is modelled as an adversarial game between a generator and a discriminator. The generator aims to create a fused image that well extracts the functional information from the GFP image and the structural information from the phase-contrast image at the same time. The target of the discriminator is to further improve the overall similarity between the fused image and the phase-contrast image. Experimental results demonstrate that the proposed method can outperform several representative and state-of-the-art image fusion methods in terms of both visual quality and objective evaluation.