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Related Experiment Video

Updated: Nov 4, 2025

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.

Adam L Tyson¹, Charly V Rousseau¹, Christian J Niedworok¹

¹Sainsbury Wellcome Centre, University College London, London, United Kingdom.

Plos Computational Biology

|May 28, 2021

Summary

This summary is machine-generated.

This study introduces an open-source algorithm for automated 3D detection of neuronal cell bodies in whole mouse brains. This method efficiently maps neuronal populations from microscopy images, overcoming manual limitations.

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Last Updated: Nov 4, 2025

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

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Published on: August 1, 2022

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

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Published on: September 5, 2018

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Area of Science:

Neuroscience
Computational Biology
Microscopy

Background:

Mapping neuronal spatial distributions is crucial for understanding nervous system function.
Manual cell mapping is time-consuming and prone to bias.
Advancements in tissue preparation and microscopy enable whole-brain imaging of cellular populations.

Purpose of the Study:

To develop an open-source algorithm for automated 3D detection of neuronal somata in whole mouse brain images.
To provide a scalable and unbiased method for mapping neuronal populations.
To demonstrate the algorithm's utility in mapping cells labeled via viral tracing.

Main Methods:

Developed an open-source algorithm for 3D neuronal somata detection.
Utilized standard desktop computer hardware for image analysis.
Applied the algorithm to whole-mouse brain microscopy images.

Main Results:

Achieved fully automated 3D detection of neuronal somata.
Successfully mapped brain-wide locations of large cell populations.
Demonstrated applicability using cells labeled via retrograde trans-synaptic viral infection.

Conclusions:

The open-source algorithm offers an efficient and unbiased approach to neuronal mapping.
This tool facilitates large-scale analysis of neuronal distributions in the mouse brain.
The method is applicable to diverse cell labeling strategies in neuroscience research.