Tyson, AL;
Rousseau, CV;
Niedworok, CJ;
Keshavarzi, S;
Tsitoura, C;
Cossell, L;
Strom, M;
(2021)
A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.
PLoS Computational Biology
, 17
(5)
, Article e1009074. 10.1371/journal.pcbi.1009074.
(In press).
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Abstract
Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.
| Type: | Article |
|---|---|
| Title: | A deep learning algorithm for 3D cell detection in whole mouse brain image datasets. |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1009074 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1009074 |
| Language: | English |
| Additional information: | © 2021 Tyson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > The Sainsbury Wellcome Centre |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10128962 |
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