This Workflow contains all the material necessary to reproduce the results of the QuPath analysis performed in the paper  "Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments" Inside this workflow and dataset, you will find the following folders QuPath Training Project: A QuPath 0.3.2 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training QuPath Demo Project: A QuPath 0.3.2 project containing an example image that can be segmented using cellpose, followed by the classification of the CD45 expressing fibers Training Images and Demo Images: The raw whole slide scanner 20x images needed by the above QuPath projects Model: The fodler contianing the trained cellpose model Cellpose Training Folder: The exported raw and ground truth images that the above cellpose model was trained on Scripts: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow QC: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics. Installation and Use If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension https://github.com/BIOP/qupath-extension-cellpose as well as a working Cellpose installation https://github.com/MouseLand/cellpose Instructions for installation are available from the links above. After that, you should be able to open the QuPath project, navigate to the "Automate > Project scripts" menu and locate the script you wish to run.