aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data.
Christian J NiedworokAlexander P Y BrownM Jorge CardosoPavel OstenSebastien OurselinMarc ModatTroy W MargriePublished in: Nature communications (2016)
The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain.
Keyphrases
- high resolution
- deep learning
- resting state
- convolutional neural network
- white matter
- functional connectivity
- artificial intelligence
- big data
- electronic health record
- machine learning
- mass spectrometry
- high speed
- tandem mass spectrometry
- data analysis
- endothelial cells
- single molecule
- cerebral ischemia
- induced pluripotent stem cells
- quantum dots
- tyrosine kinase
- liquid chromatography