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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, Lee Cossell, Molly Strom, 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
2Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, 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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Lee Cossell
1Sainsbury Wellcome Centre, University College London, 25 Howland Street, London, W1T 4JG, United Kingdom
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Molly Strom
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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Posted March 04, 2021.
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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, Lee Cossell, Molly Strom, 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, Lee Cossell, Molly Strom, Troy W. Margrie
bioRxiv 2020.10.21.348771; doi: https://doi.org/10.1101/2020.10.21.348771

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