Image Analysis Using the Fluorescence Imaging of Nuclear Staining (FINS) Algorithm.
Finding appropriate image analysis techniques for a particular purpose can be difficult. In the context of the analysis of immunocytochemistry images, where the key information lies in the number of nuclei containing co-localised fluorescent signals from a marker of interest, researchers often opt t...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 3077 - 3090 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283954&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283954 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283954 182283954 182283954 10.1007/s10278-024-01097-8 182283954 ppf: 3077 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Image Analysis Using the Fluorescence Imaging of Nuclear Staining (FINS) Algorithm. aug: au: Bramwell, Laura R. Spencer, Jack Frankum, Ryan Manni, Emad Harries, Lorna W. affil: https://ror.org/03yghzc09 RNA-Mediated Mechanisms of Disease Group, Faculty of Life Sciences, Institute of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK sug: subj: Diagnostic Imaging Evaluation Fluorescent Dyes Immunohistochemistry Methods Algorithms Staining and Labeling Human Funding Source Biological Markers Cell Proliferation DNA Damage Descriptive Statistics Cell Culture Techniques Autoanalyzers Nuclear Proteins ab: Finding appropriate image analysis techniques for a particular purpose can be difficult. In the context of the analysis of immunocytochemistry images, where the key information lies in the number of nuclei containing co-localised fluorescent signals from a marker of interest, researchers often opt to use manual counting techniques because of the paucity of available tools. Here, we present the development and validation of the Fluorescence Imaging of Nuclear Staining (FINS) algorithm for the quantification of fluorescent signals from immunocytochemically stained cells. The FINS algorithm is based on a variational segmentation of the nuclear stain channel and an iterative thresholding procedure to count co-localised fluorescent signals from nuclear proteins in other channels. We present experimental results comparing the FINS algorithm to the manual counts of seven researchers across a dataset of three human primary cell types which are immunocytochemically stained for a nuclear marker (DAPI), a biomarker of cellular proliferation (Ki67), and a biomarker of DNA damage (γH2AX). The quantitative performance of the algorithm is analysed in terms of consistency with the manual count data and acquisition time. The FINS algorithm produces data consistent with that achieved by manual counting but improves the process by reducing subjectivity and time. The algorithm is simple to use, based on software that is omnipresent in academia, and allows data review with its simple, intuitive user interface. We hope that, as the FINS tool is open-source and is custom-built for this specific application, it will streamline the analysis of immunocytochemical images. In this paper, we describe a new tool—the Fluorescence Imaging of Nuclear Staining (FINS) algorithm. This tool can automatically count images of cells that are immunocytochemically stained with a nuclear protein of interest, producing a spreadsheet of counts and a user interface for review. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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