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Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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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: Nov 24, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

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Published on: June 18, 2020

Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes.

Agnieszka Tomczak, Slobodan Ilic, Gaby Marquardt

IEEE Transactions on Medical Imaging

|December 21, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for digital staining and classification of unstained white blood cell images, simplifying diagnostics. The technique achieves state-of-the-art results by translating unstained cells into realistic stained images.

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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

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Published on: March 30, 2020

Area of Science:

Digital pathology
Medical imaging analysis
Computational biology

Background:

Traditional white blood cell staining is time-consuming.
Digital staining offers a faster, automated alternative for diagnostics.
Handling data from multiple domains with partial labeling presents challenges.

Purpose of the Study:

To develop a method for digital staining and classification of unstained white blood cell images.
To preserve crucial inter-cellular structures during image translation.
To enable automated, domain-agnostic classification of white blood cells.

Main Methods:

Proposed a translation method from unstained to realistically stained images.
Incorporated auxiliary tasks of segmentation and direct reconstruction for structure preservation.
Developed a domain-agnostic latent space by direct injection of target domain labels into the generator.

Main Results:

Achieved state-of-the-art performance in both digital staining and classification tasks.
Demonstrated superior preservation of inter-cellular structures compared to existing methods.
Validated the method on a large dataset of leukocytes from 24 patients.

Conclusions:

The proposed method effectively translates unstained white blood cell images to stained equivalents.
The approach facilitates automated diagnostics by enabling domain-invariant classification.
This digital staining technique significantly enhances efficiency and accuracy in hematological analysis.