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Updated: May 19, 2026

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies
09:32

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Published on: September 23, 2014

Image microarrays derived from tissue microarrays (IMA-TMA): New resource for computer-aided diagnostic algorithm

Jennifer A Hipp1, Jason D Hipp, Megan Lim

  • 1Department of Pathology, University of Michigan, M4233A Medical Science I, 1301 Catherine Ann Arbor, Michigan 48109-0602.

Journal of Pathology Informatics
|August 31, 2012
PubMed
Summary
This summary is machine-generated.

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This study introduces the Tissue Microarray-Image Microarray (TMA-IMA) construct, accelerating high-throughput image analysis for computer-aided diagnostic algorithms by enabling parallel interrogation of diverse tissue morphologies.

Area of Science:

  • Digital pathology
  • Computational pathology
  • Bioinformatics

Background:

  • Conventional tissue microarrays (TMAs) enable spatial arrangement of multiple patient tissue samples on a single slide.
  • Digital scanning of TMAs facilitates computer-aided diagnostic (CAD) algorithm evaluation against diverse disease morphologies.
  • Digital tools like dCORE and iMAM create image microarrays (IMAs) from TMAs for streamlined analysis.

Purpose of the Study:

  • To describe the combined use of dCORE and iMAM for generating a higher-order TMA-IMA construct.
  • To perform massively parallel image analysis on this novel construct using the SIVQ algorithm.
  • To demonstrate the utility of TMA-IMAs for accelerating CAD algorithm development and validation.

Main Methods:

  • Assembled multiple TMA cores from distinct TMAs into a single digital image montage (TMA-IMA).
Keywords:
CADIMASIVQTMAWSIdCOREiMAMimage analysis

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Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas
09:08

Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas

Published on: May 31, 2012

Related Experiment Videos

Last Updated: May 19, 2026

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies
09:32

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Published on: September 23, 2014

Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas
09:08

Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas

Published on: May 31, 2012

  • Utilized the spatially invariant vector quantization (SIVQ) algorithm for pattern matching on the TMA-IMAs.
  • Screened for specific morphologic features, such as tingible body macrophages and apoptotic bodies.

    • Main Results:

    • Successfully rendered multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia.
    • Captured distinct morphologic heterogeneity in TBM appearance and apoptotic body morphology within the IMAs.
    • Achieved excellent discriminant classification between diagnostic classes using SIVQ-based pattern matching.

    Conclusions:

    • The TMA-IMA construct accelerates high-throughput image feature discovery and classification.
    • This approach simplifies the development, validation, and comparison of CAD algorithms.
    • TMA-IMAs are particularly valuable in settings with significant diagnostic feature morphologic heterogeneity.