A deep learning algorithm for 3D cell detection in whole mouse brain image datasets
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 b...
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| Veröffentlicht in: | PLoS computational biology 2021-05, Vol.17 (5), p.e1009074-e1009074 |
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| creator | Tyson, Adam L Rousseau, Charly V Niedworok, Christian J Keshavarzi, Sepiedeh Tsitoura, Chryssanthi Cossell, Lee Strom, Molly Margrie, Troy W |
| description | 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. |
| doi_str_mv | 10.1371/journal.pcbi.1009074 |
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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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. 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| subjects | Algorithms Analysis Annotations Biology and Life Sciences Brain Candidates Computational neuroscience Computer and Information Sciences Deep learning Engineering and Technology Image analysis Image processing Image segmentation Life Sciences Machine learning Methods Microscopy Neuroimaging Neurons Neurons and Cognition Noise Object recognition Physical Sciences Research and Analysis Methods Science Policy Software Training |
| title | A deep learning algorithm for 3D cell detection in whole mouse brain image datasets |
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