DAB-quant: An open-source digital system for quantifying immunohistochemical staining with 3,3'-diaminobenzidine...

Area of Science:

• Biomedical imaging
• Computational pathology
• Digital pathology

Background:

• Immunohistochemistry (IHC) is vital for disease diagnosis and research.
• Objective quantification of IHC signals, particularly 3,3 '-diaminobenzidine (DAB), is challenging.
• Existing methods often lack objectivity and scalability for large datasets.

Purpose of the Study:

• To introduce DAB-quant, an open-source software for automated and objective IHC signal quantification.
• To provide a reliable tool for analyzing large numbers of DAB-stained tissue slides.
• To enhance the reproducibility and accuracy of IHC analysis in research.

Main Methods:

• DAB-quant utilizes Otsu's method for tissue segmentation and defines thresholds based on negative control slides.
• It calculates the fraction of stained tissue using normalized red minus blue (NRMB) color intensity.
• The software supports full slide analysis or automated random sampling for efficiency and heterogeneity assessment.

Main Results:

• DAB-quant provides objective and reproducible quantification of DAB staining intensity.
• The software generates pseudocolor images for pixel categorization and results in spreadsheets.
• Automated sampling allows for faster processing and identification of signal heterogeneity.

Conclusions:

• DAB-quant offers a robust, scalable, and accessible solution for IHC image analysis.
• The open-source nature and availability on GitHub promote widespread adoption and further development.
• This tool is expected to significantly benefit various research applications requiring precise IHC quantitation.