Cellpose training data and scripts from "Machine learning for histological annotation and quantification of cortical layers"

This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper

 "Machine learning for histological annotation and quantification of cortical layers"

Inside this workflow and dataset, you will find the following folders

1. QuPath Training Project: A QuPath 0.5.0 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training
2. Training Images and Demo Images: The raw whole slide scanner images needed by the above QuPath project
3. Model: The fodler containing the trained cellpose model
4. cellpose-training Folder: The exported raw and ground truth images that the above cellpose model was trained on
5. Scripts: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow
6. 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.

1. train a cell segmentation algorithm in the context of the rat brain Layer 
Boundaries project 

2. trigger cell segmentation from a QuPath project in a semi-automated pipeline