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Related Concept Videos

Immunocytochemistry and Immunohistochemistry01:22

Immunocytochemistry and Immunohistochemistry

Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
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A clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens.

Bonnie Hall1, Wenjin Chen, Michael Reiss

  • 1Center for Biomedical Imaging and Informatics, UMDNJ-Robert Wood Johnson Medical School, USA. huangbo@umdnj.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 30, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for analyzing immunohistochemical (IHC) staining patterns by combining protein and tissue architecture information. The approach improves automated image analysis for cancer research.

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Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Published on: January 21, 2019

Area of Science:

  • Computational pathology
  • Biomedical image analysis
  • Cancer research

Background:

  • Automated quantitative image analysis faces limitations in distinguishing intracellular immunohistochemical (IHC) staining patterns.
  • Accurate characterization of IHC staining is crucial for diagnosing and understanding diseases like cancer.

Purpose of the Study:

  • To develop a two-fold approach for IHC characterization that integrates protein stain data with tissue architecture.
  • To overcome current limitations in automated image analysis for precise IHC pattern discrimination.

Main Methods:

  • A color unmixing algorithm decomposes stained tissue sections into IHC stain and counterstain.
  • Feature measures are extracted from both staining planes, utilizing texton-based features and novel filter banks.
  • Texture signatures are derived for different IHC staining patterns.

Main Results:

  • The approach successfully classifies breast cancer tissue microarrays based on nuclear, cytoplasmic, and membrane stains.
  • Demonstrated ability to differentiate between various IHC staining patterns using combined stain and architecture features.

Conclusions:

  • The presented method offers an enhanced approach for IHC characterization by leveraging both stain and tissue architecture.
  • This technique has the potential to improve automated quantitative image analysis in pathology and cancer research.