Abstract

Manually scoring histological staining across dozens of images is time-consuming and subjective. Two researchers looking at the same slide may reach different conclusions about how much staining is present. Computational automatize quantification solves this problem: it applies the same criteria to every image, produces a numeric result, and scales to large datasets without additional effort.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• How can I quantify the percentage of stained area in histological images?
• How does color deconvolution separate individual stain components from brightfield microscopy images?
• How can I apply this workflow to IHC stained tissue sections?

Learning Objectives

• Apply color deconvolution to separate stain channels in histological images
• Extract and isolate the stain channel of interest (e.g. DAB)
• Apply automatic thresholding to distinguish stained from unstained regions
• Calculate the percentage of positively stained area relative to total tissue area
• Interpret quantitative staining results across multiple images