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Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

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Related Experiment Video

Updated: Aug 8, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Deep learning-enabled virtual histological staining of biological samples.

Bijie Bai^1,2,3, Xilin Yang^1,2,3, Yuzhu Li^1,2,3

¹Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.

Light, Science & Applications

|March 2, 2023

Summary

Virtual staining uses deep learning to digitally create histological stains from unlabeled images, offering a fast, affordable alternative to traditional methods. This technology revolutionizes tissue examination in pathology and research.

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Area of Science:

Computational pathology
Digital histology
Artificial intelligence in medicine

Background:

Histological staining is crucial for tissue and cellular structure visualization in pathology and research.
Traditional staining is costly, time-consuming, requires specialized labs, and skilled personnel.
Accessibility is limited in resource-constrained environments.

Purpose of the Study:

To provide a comprehensive overview of deep learning-enabled virtual histological staining.
To introduce the concepts and workflow of virtual staining.
To discuss recent technical innovations and future perspectives.

Main Methods:

Review of deep learning techniques for virtual histological staining.
Analysis of methods for generating stains from label-free images.
Exploration of virtual stain-to-stain transformations.

Main Results:

Deep learning successfully generates various histological stains from unstained samples.
Virtual staining offers rapid, cost-effective, and accurate alternatives.
Stain-to-stain transformation is achievable using similar AI approaches.

Conclusions:

Deep learning-enabled virtual staining presents a significant advancement over traditional methods.
This technology enhances accessibility and efficiency in tissue examination.
Future research can expand virtual staining applications across diverse scientific fields.