Slide scanner offers now the possibility to quickly and automatically digitize sections mounted onto up to 100 slides. This gives the opportunity to acquire many images with robust and reproducible parameters, with a wide range of fluorescence wavelengths, brightfield or polarization settings. It can run 24/7 and quickly produce thousands of images to be analyzed. For instance, this type of microscope is particularly suited to the field of neurosciences, since it makes it possible to image serial brain sections to compute whole brain estimates of the density of selected cell types. After acquisition, biologists end up with stacks of 2D images to be analyzed. This analysis can be performed with QuPath [1]. This open-source software is indicated for cell detection and classification in whole-slide image analysis. Among other extension, QuPath offers a bridge to the Fiji plugin ABBA [2]. Using this plugin, neuroscientists can register stacks of 2D images onto 3D reference atlases such as the ALLEN mouse brain atlas [3]. Those can be imported back to QuPath and all the brain atlas regions become available for analysis of cell counts per brain regions. Together, this approach allows neuroscientists to perform analysis on large number of brain sections and to figure out several quantitative parameters (cell density per subject/condition/brain region) thanks to R and python scripting. By the mean of the python library “brain render” [4], they will be able to display their results on a 3D view of the ALLEN mouse brain atlas. In this workshop, we will teach the entire pipeline from acquisition to image analysis (see below "educational goal" for the details).