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DiAna, an ImageJ tool for object-based 3D co-localization and distance analysis.

Jean-François Gilles1, Marc Dos Santos2, Thomas Boudier3

  • 1Sorbonne Universités, UPMC Univ Paris 06, CNRS, Core Facility - Institut de Biologie Paris Seine (IBPS), 75005 Paris, France.

Methods (San Diego, Calif.)
|November 29, 2016
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Summary

DiAna is a new ImageJ plugin for 3D image analysis. It provides robust object segmentation, co-localization, and distance analysis for biologists, enhancing biological research.

Keywords:
3D imagingDeconvolutionFluorescence microscopyImage co-localizationImage segmentationSpatial analysis

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Area of Science:

  • 3D image analysis
  • Computational biology
  • Bioimage informatics

Background:

  • Accurate 3D segmentation and co-localization are crucial for understanding biological structures and interactions.
  • Existing tools may lack the comprehensive functionality for detailed 3D distance and co-localization analysis.

Purpose of the Study:

  • To introduce DiAna, a novel ImageJ plugin for advanced 3D distance and co-localization analysis.
  • To provide biologists with an intuitive and accurate tool for quantitative 3D image analysis.

Main Methods:

  • Development of a user-friendly ImageJ plugin named DiAna.
  • Implementation of robust 3D segmentation for object extraction.
  • Automated object-based co-localization and 3D distance measurements.
  • Introduction of a novel method for assessing the statistical significance of co-localization.

Main Results:

  • DiAna enables accurate 3D object extraction and segmentation.
  • The plugin quantifies co-localization, including volumes and contact surfaces.
  • DiAna computes 3D distance distributions between objects.
  • A new method for statistical significance testing of co-localization is provided.

Conclusions:

  • DiAna is a comprehensive and intuitive 3D image analysis tool for biological research.
  • The plugin facilitates in-depth analysis of object interactions and spatial relationships in 3D.
  • DiAna enhances the capabilities for quantitative analysis in bioimage informatics.