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Related Experiment Video

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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TMARKER: A free software toolkit for histopathological cell counting and staining estimation.

Peter J Schüffler1, Thomas J Fuchs, Cheng Soon Ong

  • 1Institute for Computational Science, ETH Zurich, Switzerland ; Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, Switzerland.

Journal of Pathology Informatics
|June 15, 2013
PubMed
Summary

This study introduces TMARKER, a free software for computational pathology, enabling objective cell counting and staining estimation in cancer research. Its machine learning approach matches pathologist performance, improving diagnostic accuracy and reproducibility.

Keywords:
Color deconvolutionnuclei detectionpathologysegmentation staining estimationsuperpixel classification

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Area of Science:

  • Computational pathology
  • Biomedical image analysis
  • Machine learning in oncology

Background:

  • Manual histological analysis for cancer is time-consuming, subjective, and error-prone.
  • Variability in cell size, morphology, and staining quality complicates accurate tissue assessment.
  • Objective, computer-assisted methods are needed for reproducible clinical and research analysis.

Purpose of the Study:

  • To develop and present a novel software package for automated cell counting and staining estimation.
  • To improve the objectivity and reproducibility of immunohistochemically stained tissue analysis.
  • To provide a free, accessible tool for computational pathology.

Main Methods:

  • Utilized machine learning algorithms, including randomized decision trees and support vector machines, for nucleus detection and classification.
  • Employed superpixel segmentation to classify tissue image regions into foreground/background and malignant/benign, incorporating user feedback.
  • Integrated a color deconvolution method as a faster alternative for non-nucleus classification tasks.

Main Results:

  • The TMARKER software integrates computational pathology workflows with active learning algorithms.
  • Nucleus detection and classification performance on renal clear cell carcinoma and prostate carcinoma datasets was equivalent to that of two independent pathologists.
  • The software demonstrated robust performance in objective computational cell counting and staining estimation.

Conclusions:

  • A novel, free, and operating system-independent software package (TMARKER) for computational cell counting and staining estimation is presented.
  • This tool supports immunohistochemically stained tissue analysis in clinical practice and research, offering an alternative to expensive, proprietary systems.
  • The software's interactive learning capability allows adaptation to new image types, anticipating broader scientific application and improved reproducibility.