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Related Experiment Video

Updated: Jul 19, 2025

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.

Xin Liu1,2, Boyi Li1, Chengcheng Liu1

  • 1Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China.

Phenomics (Cham, Switzerland)
|August 17, 2023
PubMed
Summary

This study introduces a deep learning method for fluorescence imaging of tissues, reducing preparation time and costs. The approach uses a conditional generative adversarial network (cGAN) for virtual multi-label fluorescent staining.

Keywords:
Generative adversarial networkImage translationTissues sectionVirtual fluorescence labeling

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Area of Science:

  • Histopathology
  • Biomedical Imaging
  • Computational Biology

Background:

  • Fluorescence labeling and imaging are vital for observing biological tissue structure in histopathology.
  • Current methods face challenges including time-consuming preparation, high reagent costs, and photobleaching-induced signal bias.

Purpose of the Study:

  • To develop a deep learning-based method for fluorescence translation of tissue sections.
  • To overcome limitations of traditional fluorescence imaging techniques.

Main Methods:

  • A conditional generative adversarial network (cGAN) was employed for fluorescence translation.
  • The method was experimentally validated using mouse kidney tissues.

Main Results:

  • The proposed method successfully predicts different fluorescence images from a single raw image.
  • Virtual multi-label fluorescent staining was achieved by merging generated images.
  • Significant reduction in preparation time, cost, and labor was demonstrated.

Conclusions:

  • Deep learning, specifically cGANs, offers an efficient solution for fluorescence imaging in histopathology.
  • The method enables virtual multi-label staining, saving resources and time.
  • This approach enhances the practicality and accessibility of fluorescence imaging for tissue analysis.