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

View ORCID ProfileAdam L. Tyson, Charly V. Rousseau, Christian J. Niedworok, Sepiedeh Keshavarzi, Chryssanthi Tsitoura, Troy W. Margrie
doi: https://doi.org/10.1101/2020.10.21.348771
Adam L. Tyson
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
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  • ORCID record for Adam L. Tyson
Charly V. Rousseau
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
2Institute Pasteur, 25 Rue du Dr Roux, 75015 Paris, France
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Christian J. Niedworok
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
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Sepiedeh Keshavarzi
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
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Chryssanthi Tsitoura
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
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Troy W. Margrie
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
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  • For correspondence: t.margrie@ucl.ac.uk
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/brainglobe/cellfinder

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 21, 2020.
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A deep learning algorithm for 3D cell detection in whole mouse brain image datasets
Adam L. Tyson, Charly V. Rousseau, Christian J. Niedworok, Sepiedeh Keshavarzi, Chryssanthi Tsitoura, Troy W. Margrie
bioRxiv 2020.10.21.348771; doi: https://doi.org/10.1101/2020.10.21.348771
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A deep learning algorithm for 3D cell detection in whole mouse brain image datasets
Adam L. Tyson, Charly V. Rousseau, Christian J. Niedworok, Sepiedeh Keshavarzi, Chryssanthi Tsitoura, Troy W. Margrie
bioRxiv 2020.10.21.348771; doi: https://doi.org/10.1101/2020.10.21.348771

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