QuPath H-DAB Project
  • ๐Ÿ“ŠQuPath guide for H-DAB Cell Counting Docs
  • ๐Ÿ’กPipeline
    • ๐Ÿ’กSimple Pipeline
  • Fundamentals
    • ๐Ÿ› ๏ธGetting Set Up
  • QuPath H-Dab Docs
    • ๐Ÿ’กQuPath H-DAB Tutorial
      • Creating and Opening of Projects
      • Estimating Stain Vectors
      • Training Image Creation
      • Cell Detection
      • Object Classifier
      • Tissue Detection
        • 1๏ธโƒฃAutomatic General Tissue Detection
        • 2๏ธโƒฃManual Specific Tissue Detection
    • ๐ŸงพQuPath Script
      • Batch Processing
    • ๐Ÿ“‚Output
  • Result Analysis Docs
    • ๐Ÿ’กProcessing Package Tutorial
      • Addition Information for Python insiders
      • In-Depth Python usage
    • ๐Ÿ—„๏ธFinal Spreadsheet creation
    • ๐Ÿ†˜Out-of-memory error
  • non-expert docs
    • ๐Ÿ”ŽQuPath Installation
    • ๐Ÿ’ปGit Bash Installation
    • ๐Ÿ–ฅ๏ธPython Installation & Packages
    • Git Bash installation
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  1. QuPath H-Dab Docs
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  3. Tissue Detection

Automatic General Tissue Detection

This will lead to one uniform immunopositivity rate throughout all the detected tissue within the slide.

PreviousTissue DetectionNextManual Specific Tissue Detection

Last updated 9 months ago

Make sure to open the Project_Regions for this part.

  1. Under Classify, choose pixel classification, create thresholder

    The following parameters can be adjusted:

The colour will indicate what is detected as "Region" and what needs to be "Ignored*".

The threshold should be tested on multiple slides with different intensities to ensure optimal tissue detection for the entire batch.

  1. The classifier needs to be saved as TissueDetection to run the script.

The classifier needs to be saved with the same spelling "TissueDetection" as this is used in the script.

  1. Click on the three little dots in the right corner, and run the script for the whole project (with saving). The script uses your predefined thresholder, to detect the tissue and removes in another line the sample's edges, as staining often shows an edge effect.

  2. After the automatic detection is run, each slide should be checked and if necessary regions can be removed from the analysis (shown in the image below)

As a next step download () the script and copy it into your project-region folder. Click Ctrl+L, open up the script editor under File, Open, open the downloaded script.

Replace the SetColorDeconvolustionStains with the values from your own EstimateStainingVector values, saved in a script.

๐Ÿ’ก
1๏ธโƒฃ
previously

Resolution

Choose the lowest that still gives you an adequate detection

Channel

Choose average channels do detect all the tissue

If interested in only hematoxylin or DAB choose adequate

Prefilter

Gaussian ; performs weighted average of surrounding pixel based on gaussian distribution (chosen in our case)

Smoothing sigma

Defines the amount of blurring

Threshold

Set threshold that it detect the tissue

In the above example it is at 250

Above threshold

Set Class Ignore*

Below threshold

Set Class Region

Region

Everywhere

Classifier name

TissueDetection

AutomaticTissueDetetion.groovy
TissueDetection thresholder
Script Editor, and how to run it for the whokle project
Tissue folds and other regions of no interest can be removed