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Microglia are known to play important roles in brain development and homeostasis, yet their molecular regulation is still poorly understood. Identification of microglia regulators is facilitated by genetic screening and studying the phenotypic effects in animal models. Zebrafish are ideal for this, as their external development and transparency allow in vivo imaging by bright-field microscopy in the larval stage. However, manual analysis of the images is very labor intensive. Here we present a computational method to automate the analysis. It merges the optical sections into an all-in-focus image to simplify the subsequent steps of segmenting the brain region and detecting the contained microglia for quantification and downstream statistical testing. Evaluation on a fully annotated data set of 50 zebrafish larvae shows that the method performs close to the human expert.
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Automated Quantitative Analysis of Microglia in Bright-Field Images of Zebrafish
Microglia are known to play important roles in brain development and homeostasis, yet their molecular regulation is still poorly understood. Identification of microglia regulators is facilitated by genetic screening and studying the phenotypic effects in animal models. Zebrafish are ideal for this, as their external development and transparency allow in vivo imaging by bright-field microscopy in the larval stage. However, manual analysis of the images is very labor intensive. Here we present a computational method to automate the analysis. It merges the optical sections into an all-in-focus image to simplify the subsequent steps of segmenting the brain region and detecting the contained microglia for quantification and downstream statistical testing. Evaluation on a fully annotated data set of 50 zebrafish larvae shows that the method performs close to the human expert.