QuPath H-DAB Project
  • 📊QuPath guide for H-DAB Cell Counting Docs
  • 💡Pipeline
    • 💡Simple Pipeline
  • Fundamentals
    • 🛠️Getting Set Up
  • QuPath H-Dab Docs
    • 💡QuPath H-DAB Tutorial
      • Creating and Opening of Projects
      • Estimating Stain Vectors
      • Training Image Creation
      • Cell Detection
      • Object Classifier
      • Tissue Detection
        • 1️⃣Automatic General Tissue Detection
        • 2️⃣Manual Specific Tissue Detection
    • 🧾QuPath Script
      • Batch Processing
    • 📂Output
  • Result Analysis Docs
    • 💡Processing Package Tutorial
      • Addition Information for Python insiders
      • In-Depth Python usage
    • 🗄️Final Spreadsheet creation
    • 🆘Out-of-memory error
  • non-expert docs
    • 🔎QuPath Installation
    • 💻Git Bash Installation
    • 🖥️Python Installation & Packages
    • Git Bash installation
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  1. QuPath H-Dab Docs
  2. QuPath H-DAB Tutorial

Creating and Opening of Projects

Easiest way to start a batch analysis is to start a project on QuPath, specific for each antibody.

PreviousQuPath H-DAB TutorialNextEstimating Stain Vectors

Last updated 9 months ago

After opening QuPath, these are the steps to follow:

  1. Create project › Select empty folder (e.g IL-1)

  1. Add images (to the new Created project) ›

    • Image provider -> default

    • Set image type -> H-DAB

    • Choose files -> Select slide scans from specific stain (e.g. IL1)

This might take a while depending on how many images you are importing and on the processor of your laptop.

  1. Create two projects containing identical slides; one to perform all the training on (xx_Calssifiers); and the other for running the final analysis within the specific regions. (xx_Regions). The first part of this guide will be performed in the Classifiers project, the final analysis will be performed in the Regions project. It is clearly stated when to switch and what to copy from the Classifiers to the Regions project folder.

  1. Open project › Select “project.qpproj” in IL1

    (This file is automatically generated in Step 1)

  1. Now open up the _Classifier project

💡
Folder system to perform the analysis