Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical staining

Sci Rep. 2017 Feb 21;7:42964. doi: 10.1038/srep42964.

Abstract

Immunohistochemistry (IHC) is a widely used technique in pathology to evidence protein expression in tissue samples. However, this staining technique is known for presenting inter-batch variations. Whole slide imaging in digital pathology offers a possibility to overcome this problem by means of image normalisation techniques. In the present paper we propose a methodology to objectively evaluate the need of image normalisation and to identify the best way to perform it. This methodology uses tissue microarray (TMA) materials and statistical analyses to evidence the possible variations occurring at colour and intensity levels as well as to evaluate the efficiency of image normalisation methods in correcting them. We applied our methodology to test different methods of image normalisation based on blind colour deconvolution that we adapted for IHC staining. These tests were carried out for different IHC experiments on different tissue types and targeting different proteins with different subcellular localisations. Our methodology enabled us to establish and to validate inter-batch normalization transforms which correct the non-relevant IHC staining variations. The normalised image series were then processed to extract coherent quantitative features characterising the IHC staining patterns.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / metabolism
  • Glioblastoma / metabolism
  • Glioblastoma / pathology
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Immunohistochemistry / methods*
  • Tissue Array Analysis

Substances

  • Biomarkers, Tumor