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Updated: Jun 23, 2025

Using Computer Vision Libraries to Streamline Nuclei Quantification
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Image Analysis Using the Fluorescence Imaging of Nuclear Staining (FINS) Algorithm.

Laura R Bramwell1, Jack Spencer2, Ryan Frankum1

  • 1RNA-Mediated Mechanisms of Disease Group, Faculty of Life Sciences, Institute of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.

Journal of Imaging Informatics in Medicine
|June 17, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed the Fluorescence Imaging of Nuclear Staining (FINS) algorithm to quantify fluorescent signals in immunocytochemistry images. This open-source tool streamlines cell analysis, offering consistent results with reduced subjectivity and time compared to manual counting.

Keywords:
Automated cell countingFluorescence imagingKi67Nuclear stainγH2AX

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Area of Science:

  • Biomedical Imaging
  • Cell Biology
  • Computational Biology

Background:

  • Immunocytochemistry is crucial for visualizing cellular markers, but analysis, especially counting nuclei with co-localized signals, is often manual and time-consuming.
  • A lack of specialized, accessible tools hinders efficient and objective quantification of fluorescent signals in immunocytochemistry images.
  • Manual counting of fluorescently labeled nuclei in immunocytochemistry images is prone to subjectivity and significant time investment.

Purpose of the Study:

  • To develop and validate a novel algorithm, Fluorescence Imaging of Nuclear Staining (FINS), for accurate quantification of fluorescent signals in immunocytochemistry.
  • To provide an open-source, user-friendly tool to automate and standardize the analysis of immunocytochemical images.
  • To compare the performance of the FINS algorithm against manual counting methods in terms of accuracy and efficiency.

Main Methods:

  • The FINS algorithm utilizes variational segmentation of the nuclear stain channel (DAPI) and iterative thresholding to identify and count co-localized fluorescent signals.
  • The algorithm was validated using immunocytochemistry data from three human primary cell types stained for DAPI, Ki67 (proliferation marker), and γH2AX (DNA damage marker).
  • Experimental results were compared against manual counts performed by seven researchers to assess consistency and acquisition time.

Main Results:

  • The FINS algorithm demonstrated quantitative performance consistent with manual counting data across different cell types and markers.
  • FINS significantly reduced the time required for image analysis compared to manual counting methods.
  • The algorithm minimized subjectivity inherent in manual counting, leading to more reproducible results.

Conclusions:

  • The FINS algorithm offers a reliable, efficient, and objective method for quantifying fluorescent signals in immunocytochemistry images.
  • This open-source tool has the potential to streamline research workflows in cell biology and biomedical imaging.
  • FINS provides an accessible solution for researchers, improving the analysis of nuclear protein co-localization in stained cells.