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Related Experiment Video

Updated: Jul 24, 2025

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Published on: May 11, 2015

A deep convolutional neural network for efficient microglia detection.

Ilida Suleymanova¹, Dmitrii Bychkov², Jaakko Kopra³

¹Faculty of Biological and Environmental Sciences, Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland. ilida.suleymanova@helsinki.fi.

Scientific Reports

|July 10, 2023

Summary

This summary is machine-generated.

Researchers developed an automated method using YOLOv3 deep learning to accurately count microglia, essential brain cells involved in diseases. This efficient tool aids neuroscience research by overcoming limitations in current microglia detection methods.

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Last Updated: Jul 24, 2025

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

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Published on: May 11, 2015

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Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

Area of Science:

Neuroscience
Immunology
Computational Biology

Background:

Microglial cells are crucial in neurodegenerative and cardiovascular diseases.
Accurate microglia counting from immunohistological images is challenging due to morphological heterogeneity and limitations of current methods.
Existing automated methods lack efficiency and precision.

Purpose of the Study:

To develop and validate a fully automated, efficient, and accurate method for microglia detection using deep learning.
To address the limitations of current image analysis techniques for microglia quantification.
To provide a valuable tool for neuroscience research in disease models.

Main Methods:

Development of a microglia detection method based on the YOLOv3 deep learning algorithm.
Application of the method to analyze microglia counts in rat spinal cord and brain regions.
Validation of the method's performance against existing computational and manual approaches.

Main Results:

The YOLOv3-based method achieved high accuracy in automated microglia detection.
Performance metrics included 94% precision, 91% recall, and 92% F1-score.
The developed tool demonstrated superior performance compared to existing methods.

Conclusions:

The study successfully developed and validated an efficient and accurate automated microglia detection tool.
The freely available tool enhances the study of various disease models in neuroscience.
This deep learning-based approach offers a significant advancement for microglia quantification in research.