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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  2. QuPath H-DAB Tutorial
  3. Tissue Detection

Manual Specific Tissue Detection

Distincton between different region within one sample.

PreviousAutomatic General Tissue DetectionNextQuPath Script

Last updated 10 months ago

Make sure to open the Project_Regions for this part.

  1. Add the classes needed to distinguish the regions. Remember if you click on Auto set the class will stay the chosen one once you start drawing regions.

  2. Manually draw and classify each region in every slide and save it. Different tools to draw can be used, see what works best for you.

  3. Make sure to save the changes to the slides when you switch from one to the other, this ensures that your specific regions stay.

Regions with tissue folds or excessive staining can be excluded from the analysis.

With this manual step, an immunopositivity rate for each tissue region will be the output.

๐Ÿ’ก
2๏ธโƒฃ
Whole bovine disc, shown with the manual detection for each tissue component