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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

Published on: March 30, 2020

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning.

Pranita Pradhan^1,2, Tobias Meyer², Michael Vieth³

¹Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany.

Biomedical Optics Express

|May 17, 2021

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This study introduces computational staining of non-linear multimodal images to mimic Hematoxylin and Eosin (H&E) staining. This deep learning approach accelerates disease diagnosis by eliminating lengthy traditional tissue preparation.

Area of Science:

Histopathology
Medical Imaging
Computational Biology

Background:

Hematoxylin and Eosin (H&E) staining is the gold-standard in histopathology but requires extensive sample preparation, hindering real-time disease diagnosis.
Non-linear multimodal (NLM) imaging offers a label-free alternative, combining multiple optical modalities for rapid tissue analysis.

Purpose of the Study:

To develop computational staining methods for NLM images to replicate H&E staining.
To enable real-time disease diagnosis by correlating NLM imaging with traditional histopathology.

Main Methods:

Utilized deep learning models, specifically conditional generative adversarial networks (CGANs) for supervised and cycle CGANs for unsupervised approaches.
Employed NLM imaging, integrating coherent anti-Stokes Raman scattering, two-photon excitation fluorescence, and second-harmonic generation.

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Quantitatively analyzed generated pseudo H&E images using metrics like mean squared error and structure similarity index.

Main Results:

Both supervised (CGAN) and unsupervised (cycle CGAN) models successfully generated pseudo H&E images from NLM data.
Quantitative analysis demonstrated significant performance of the computational staining methods.
This represents the first unsupervised GAN-based computational staining of NLM images to H&E.

Conclusions:

Computational staining of NLM images using CGANs and cycle CGANs is a viable and beneficial approach for diagnostic applications.
This method bypasses the need for traditional laboratory-based staining procedures, offering a faster alternative.
The developed technique holds promise for accelerating real-time disease diagnosis in histopathology.