A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain

<strong>Quality-controlled predictions of deep learning models for cell counting</strong> This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper: <em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em> The dataset integrates the raw data published on a previous upload on Zenodo. Cell locations were obtained using two deep-learning models for cell counting (publicly available on GitHub, details in the paper by Ciampi et al., 2022). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. <strong>Content</strong> The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper. <strong>Folder Structure and file naming conventions</strong> There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).