RT Journal Article SR Electronic T1 A deep learning algorithm for 3D cell detection in whole mouse brain image datasets JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.10.21.348771 DO 10.1101/2020.10.21.348771 A1 Adam L. Tyson A1 Charly V. Rousseau A1 Christian J. Niedworok A1 Sepiedeh Keshavarzi A1 Chryssanthi Tsitoura A1 Lee Cossell A1 Molly Strom A1 Troy W. Margrie YR 2021 UL http://biorxiv.org/content/early/2021/03/04/2020.10.21.348771.abstract AB 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. How-ever, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an opensource 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 StatementThe authors have declared no competing interest.