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Mapping the structure of the mammalian brain at cellular resolution is a challenging task and one that requires capturing key anatomical features at the appropriate level of analysis. Although neuroscientific methods have managed to provide significant insights at the micro and macro level, in order to obtain a whole-brain analysis at a cellular resolution requires a meso-scopic approach. A number of methods can be currently used to detect and count cells, with, nevertheless, significant limitations when analyzing data of high complexity. To overcome some of these constraints, we introduce a fully automated Artificial Intelligence (AI)-based method for whole-brain image processing to  De tect  Ne urons in different brain  R egions during  D evelopment ( DeNeRD ). We demonstrate a high performance of our deep neural network in detecting neurons labeled with different genetic markers in a range of imaging planes and imaging modalities.

DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning