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Related Experiment Video

Updated: May 25, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Supervised learning framework for screening nuclei in tissue sections.

Kaustav Nandy1, Prabhakar R Gudla, Ryan Amundsen

  • 1Optical Microscopy and Analysis Laboratory, Advanced Technology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA. nandyk@mail.nih.gov

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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Automated cell nuclei segmentation using artificial neural networks (ANNs) provides a fast and accurate method for analyzing large biological image datasets. This approach overcomes the subjectivity and time constraints of manual segmentation in tissue sections.

Area of Science:

  • Biomedical image analysis
  • Computational pathology
  • Artificial intelligence in biology

Background:

  • Accurate cell nuclei segmentation is crucial for biological and clinical applications.
  • Manual segmentation of large image datasets is subjective and time-consuming.
  • Automated techniques are needed for high-throughput analysis.

Purpose of the Study:

  • To develop an automated supervised learning framework for accurate cell nuclei segmentation.
  • To utilize artificial neural networks (ANNs) within a multistage watershed segmentation algorithm.
  • To validate the framework's performance on human breast tissue section images.

Main Methods:

  • Implementation of a supervised learning framework using artificial neural networks (ANNs).

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Last Updated: May 25, 2026

Using Computer Vision Libraries to Streamline Nuclei Quantification
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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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  • Integration of ANNs with a multistage watershed segmentation algorithm.
  • Application and comparison of the framework on 9 human breast tissue image datasets.

    • Main Results:

    • Successful automation of cell nuclei segmentation was demonstrated.
    • Over 1400 well-segmented nuclei were screened.
    • The ANN framework showed comparable or improved performance against a previous stacked classifier method.

    Conclusions:

    • The developed ANN-based framework offers an automated, robust, and accurate solution for cell nuclei segmentation.
    • This method facilitates high-throughput analysis of large microscopic image datasets.
    • The approach holds significant potential for biological and clinical applications requiring precise nuclear identification.