A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.
Adam L TysonCharly V RousseauChristian J NiedworokSepiedeh KeshavarziChryssanthi TsitouraLee CossellMolly StromTroy W MargriePublished in: PLoS computational biology (2021)
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.
Keyphrases
- deep learning
- high resolution
- label free
- induced apoptosis
- gene expression
- convolutional neural network
- artificial intelligence
- machine learning
- resting state
- white matter
- cell cycle arrest
- cerebral ischemia
- high throughput
- functional connectivity
- single molecule
- optical coherence tomography
- single cell
- loop mediated isothermal amplification
- high speed
- stem cells
- oxidative stress
- real time pcr
- multiple sclerosis
- cell therapy
- quantum dots
- positron emission tomography
- genetic diversity
- rna seq
- brain injury
- cell proliferation