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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: Apr 21, 2026

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

Published on: May 1, 2019

Appearance Normalization of Histology Slides.

Marc Niethammer, David Borland, J S Marron

Machine Learning in Medical Imaging. MLMI (Workshop)

|November 1, 2014

Summary

This study introduces automatic color and intensity normalization for histology slides using stain vectors. This method enhances stability and standardizes slide appearance, crucial for digital pathology applications.

Area of Science:

Digital pathology
Histology image analysis
Computational imaging

Background:

Histology slides often exhibit color and intensity variations due to staining protocols and fading.
Standardizing slide appearance is critical for reproducible analysis in digital pathology.
Previous normalization methods lack robustness and can be sensitive to variations in stain types.

Purpose of the Study:

To develop an automatic color and intensity normalization method for digitized histology slides.
To improve the stability and accuracy of normalization using prior stain vector information.
To provide a practical solution for standardizing histology slide appearance.

Main Methods:

A novel method for automatic color and intensity normalization of digitized histology slides.

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Last Updated: Apr 21, 2026

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Utilizes prior information on stain vectors within the estimation process.

Validated using synthetic experiments and 13 real-world histology datasets.

Main Results:

The proposed method demonstrates improved stability in normalization estimates compared to previous approaches.
Effective in countering variations caused by differing stain amounts, protocols, and slide fading.
Successfully validated on diverse datasets, highlighting practical utility.

Conclusions:

The developed method offers a robust and stable approach for automatic histology slide normalization.
Significant practical utility for digital pathology, particularly for hematoxylin and eosin-stained slides.
Enables standardization of slide appearance, enhancing downstream image analysis and interpretation.