Empowering Open Science: Eliceiri led team Secures NIGMS R24 Grant to Advance Image Analysis Tools

In the era of big data, biomedical imaging has become one of the most powerful windows into the inner workings of life. From tracking cancer cells to mapping neural networks, researchers rely on advanced imaging technologies to generate massive datasets. But making sense of these images—extracting meaningful patterns and quantifiable insights—requires sophisticated analysis tools. For decades, open-source platforms like ImageJ, Fiji and CellProfiler have been the backbone of this effort, democratizing access to image analysis for scientists worldwide.

Now, thanks to a major new award from the National Institute of General Medical Sciences (NIGMS), these tools are poised for a transformative leap forward.

In September, Kevin Eliceiri, professor of Medical Physics and Biomedical Engineering at the University of Wisconsin–Madison, investigator at the Morgridge Institute for Research and director of the Center for Quantitative Cell Imaging (CQCI), secured an NIGMS R24 National Resource grant to strengthen and expand open-source image analysis infrastructure. This multi-year grant, awarded in collaboration with co-PI Beth Cimini, associate director, Bioimage Analysis, Imaging Platform, at the Broad Institute, will ensure that Fiji, ImageJ, CellProfiler and related resources remain cutting-edge, sustainable, and accessible to the global research community.

A National Resource for Open Science

The NIGMS R24 program supports resources that serve a broad national user base, providing state-of-the-art technologies, software and training. Unlike traditional research grants, these awards focus on infrastructure—building and maintaining tools that thousands of scientists depend on every day.

“This grant is about more than software,” says Eliceiri. “It’s about sustaining a community and ensuring that researchers everywhere have access to reliable, reproducible, and innovative image analysis tools.”

The project will enhance Fiji, ImageJ and CellProfiler by integrating advanced features such as more deep learning workflows, improving interoperability with other scientific platforms, and streamlining usability for both novice and expert users. These upgrades will help researchers tackle increasingly complex imaging challenges, from 3D tissue reconstruction to high-throughput screening.

“I’m delighted to work with Kevin and his outstanding team to continue improving bioimage analysis and making analyzing images easier and more accurate for scientists at every computational comfort level,” says Cimini, who leads the Cimini Lab within the Imaging Platform of the Broad Institute of MIT and Harvard. “The first 6 years of our joint Center for Open Bioimage Analysis have led created great software and educational resources for hundreds of thousands of scientists – it will be exciting to see what the next 5 years together can bring!”

Why Fiji and ImageJ Matter

ImageJ began as a simple image-processing program developed at the NIH in the late 1990s. Its open-source nature and plugin architecture quickly made it indispensable for life scientists. Fiji—short for “Fiji Is Just ImageJ”—built on that foundation, adding curated plugins and workflows tailored for biological imaging.

Today, these platforms are used by tens of thousands of researchers worldwide. They enable everything from basic measurements to sophisticated analyses like cell segmentation, fluorescence quantification, and time-lapse tracking. Their openness means that any lab, regardless of budget, can access powerful tools without costly licenses.

“Open-source tools level the playing field,” Eliceiri explains. “They allow small labs to do big science and foster collaboration across disciplines and continents.”

The Broad Institute Partnership

The collaboration with co-PI Beth Cimini of the Broad Institute brings additional expertise in software engineering and large-scale data management. Importantly this also includes support for the continued development of CellProfiler, and Piximi, two additional user accessible tools for image analysis.  Together, the teams will focus on making Fiji and ImageJ more robust for high-volume workflows and cloud-based environments—critical for handling the terabytes of data generated by modern imaging systems.

This partnership also underscores a shared commitment to open science. By keeping these tools free and community-driven, the project aligns with global efforts to improve reproducibility and transparency in biomedical research.

Impact on the Scientific Community

The benefits of this grant extend far beyond software development. The project includes a strong emphasis on training and outreach, offering workshops, online tutorials, and user support to help scientists harness the full potential of these tools.

Key impact areas include:

• Innovation: Incorporating AI-driven algorithms for smarter image segmentation and pattern recognition.
• Accessibility: Simplifying interfaces and workflows so that even non-specialists can analyze complex datasets.
• Collaboration: Strengthening ties between UW–Madison, Broad Institute, and international imaging communities.

By investing in these resources, NIGMS is ensuring that the next generation of biomedical discoveries will be powered by tools that are not only powerful but also open and reproducible.

Looking Ahead

For Eliceiri and his team, the grant represents both a validation of past efforts and a challenge for the future. Imaging technologies are evolving rapidly, and analysis tools must keep pace. The roadmap includes integrating cloud-based workflows, supporting big-data pipelines, and embedding reproducibility standards into every layer of the software.

“The future of imaging is collaborative and data-driven,” Eliceiri says. “Our goal is to make sure the tools scientists need are not locked behind paywalls or proprietary systems. Open-source infrastructure accelerates discovery for everyone.”

“As biomedical research continues to push boundaries—from mapping entire organs in 3D to decoding cellular dynamics in real time—the importance of accessible, reliable image analysis cannot be overstated,” Eliceiri adds. “Thanks to this NIGMS R24 grant, the global scientific community will have the resources it needs to turn images into insights—and insights into breakthroughs.”

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