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Related Experiment Video

Updated: Jun 20, 2026

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
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Published on: May 24, 2022

SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.

Carlos García-López-de-Haro1,2, Caterina Fuster-Barceló3,4, Curtis T Rueden5

  • 1Neuroscience and Life Sciences Department, Universidad Carlos III de Madrid, Getafe, Spain.

Nature Communications
|June 18, 2026
PubMed

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Summary

This study introduces SAMJ, a user-friendly ImageJ/Fiji plugin for fast, accurate biomedical image annotation using artificial intelligence (AI). SAMJ simplifies the creation of AI training datasets, making advanced tools accessible to researchers.

Area of Science:

  • Biomedical image analysis
  • Artificial intelligence in medicine
  • Bioimage informatics

Background:

  • Accurate AI training for biomedical image analysis requires time-consuming pixel-level annotations.
  • Existing foundation models like Segment Anything Model (SAM) have limited integration into bioimage analysis platforms.
  • Technical expertise and specialized hardware are often needed for advanced AI annotation.

Purpose of the Study:

  • To develop a user-friendly plugin for ImageJ/Fiji that simplifies AI-assisted image annotation.
  • To enable fast, interactive, and accurate annotation of biomedical images without programming skills.
  • To broaden access to advanced AI annotation tools for the research community.

Main Methods:

  • Integration of efficient Segment Anything Model (SAM) variants into ImageJ/Fiji via the SAMJ plugin.

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  • Development of a graphical user interface for interactive object segmentation using clicks or bounding boxes.
  • Implementation on standard computers without requiring specialized hardware.

    • Main Results:

    • SAMJ enables real-time delineation of objects in large scientific images.
    • The plugin significantly reduces the effort and time required for image annotation.
    • SAMJ facilitates faster creation of datasets for AI model training.

    Conclusions:

    • SAMJ provides a fast, interactive, and accurate solution for biomedical image annotation.
    • The plugin lowers the barrier to entry for using AI-assisted annotation tools in research.
    • SAMJ accelerates AI development in biomedical image analysis by simplifying dataset creation.