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The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis.

Alexandra B Schroeder1,2,3, Ellen T A Dobson1, Curtis T Rueden1

  • 1Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin, USA.

Protein Science : a Publication of the Protein Society
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Summary
This summary is machine-generated.

ImageJ, an open-source image analysis platform, has evolved with new plugins and tools to handle complex biological imaging data. Recent updates, including deep learning capabilities and ImageJ2 enhancements, improve multidimensional image processing and interoperability.

Keywords:
FijiImageJimage analysisimagingmicroscopyopen source software

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Area of Science:

  • Bioimage analysis
  • Computational biology
  • Scientific image processing

Background:

  • Biologists use software for imaging data interpretation.
  • Increasingly complex imaging techniques generate large, multidimensional datasets.
  • ImageJ is a widely adopted open-source platform for image analysis.

Purpose of the Study:

  • To review the ImageJ ecosystem's core features.
  • To highlight ImageJ's adaptations to advancements in imaging technology.
  • To discuss the impact of ImageJ2 on image processing and interoperability.

Main Methods:

  • Review of ImageJ ecosystem components and plugins.
  • Analysis of recent adaptations to address user needs in image analysis.
  • Examination of ImageJ2's architectural changes and contributions.

Main Results:

  • ImageJ has adapted to complex imaging data with new plugins for visualization, segmentation, and tracking.
  • Deep learning capabilities are being integrated into ImageJ, reflecting a trend towards AI in bioimage analysis.
  • ImageJ2 has enhanced multidimensional image processing and interoperability within the ImageJ ecosystem.

Conclusions:

  • The ImageJ ecosystem, driven by its user and developer communities, continues to evolve to meet the demands of modern biological imaging.
  • Recent developments, particularly those facilitated by ImageJ2, have significantly improved ImageJ's ability to handle large, complex datasets and integrate advanced analytical techniques like deep learning.
  • ImageJ remains a vital tool for biological research, adapting to technological advancements and fostering community-driven innovation in image analysis.