💡QuPath H-DAB Tutorial

This is the summary page for all the steps involved in this tutorial.

To follow this tutorial you do not need to be familiar with QuPath. Everything needed to perform the final analysis is explained in a step-by-step approach. If you haven't installed QuPath yet please visit this section here.

The slide scanning results in a new file name (ID_Slidescanning) e.g. slide-2023-06-01T12-10-21-R3-S1. Throughout the whole analysis, this filename is kept and the sample ID is not directly known but saved in an Excel spreadsheet. After the QuPath H-DAB script is run the sample ID and the slide scan ID are linked.

These docs are a detailed tutorial on how to interact with QuPath and how it is used to train an Object Classifier for the detection and classification of H-DAB Cells on Intervertebral Disc tissue, which can be applied to similar tissue types.

If you want to train your classifier or are curious to how it was trained, please follow the steps:

• Step 1: Create a new project/ open project;

• Step 2: Estimation of staining vectors;

• Step 3: Create a training image (for Object Classifier Training);

• Step 4: Cell Detection;

• Step 5: Training the Object Classifier;

• Step 6: Tissue Detection:

  • Automatic general tissue detection,

  • Manual specific tissue type detection.

• Step 7: Batch processing with the script

This manual is specifically written for the analysis of immunohistochemically stained samples derived from tissues with low cellularity. QuPath has a general and detailed introduction website which we advise you to visit in case you have a deeper interest in the software or have immunofluorescence samples to analyse.

In this section, you can find the tutorial using the QuPath H-DAB Model already trained and how to use it on QuPath.

License:

Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports (2017). https://doi.org/10.1038/s41598-017-17204-5