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Related Experiment Video

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Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts

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Noninvasive Nonlinear Optical Computational Histology.

Binglin Shen¹, Zhenglin Li¹, Ying Pan²

¹Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|December 14, 2023

Summary

A new stain-free computational histology method uses label-free nonlinear optical imaging and contrastive learning for rapid, precise cancer diagnosis. This approach enhances early detection and reduces costs in surgical pathology.

Keywords:

Stimulated Raman scattering microscopy cancer diagnosis deep learning multiphoton microscopy nonlinear optical imaging

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Area of Science:

Biomedical Imaging
Computational Pathology
Artificial Intelligence in Medicine

Background:

Cancer diagnosis relies heavily on histopathology, which typically requires tissue staining.
Staining procedures can be time-consuming, costly, and may introduce artifacts.
There is a need for rapid, accurate, and non-invasive diagnostic methods in cancer pathology.

Purpose of the Study:

To introduce a novel stain-free computational histology approach called nonlinear optical computational histology (NOCH).
To evaluate the efficacy of NOCH in preserving key diagnostic features from fresh tissues using label-free nonlinear optical imaging.
To demonstrate the potential of NOCH for improving the speed and accuracy of cancer diagnosis in surgical pathology.

Main Methods:

Utilizing label-free nonlinear optical imaging modalities, including stimulated Raman scattering and multiphoton imaging.
Applying contrastive patch-wise learning for stain-free computational histology.
Performing quantitative analysis to assess the preservation of nuclear morphometric features and diagnostic characteristics.

Main Results:

NOCH accurately reproduces nuclear morphology, size, and nuclear-cytoplasmic contrast across different cancer stages.
The method demonstrates high sensitivity in analyzing the tumor microenvironment.
NOCH models show promising generalization capabilities when applied to various pathological tissues.

Conclusions:

NOCH offers a rapid, non-invasive, and precise alternative to traditional staining methods in surgical pathology.
This stain-free computational histology approach has significant potential to revolutionize cancer diagnosis and surgical interventions.
The integration of label-free nonlinear optical imaging with contrastive learning paves the way for advanced computational pathology.