The ImageJ ecosystem: an open platform for biomedical image analysis

By Johannes E. Schindelin, Curtis T. Rueden, Mark C. Hiner, Kevin W. Eliceiri. Molecular Reproduction and Development, 2015

2,514 citations recorded by the source corpus.

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more‐advanced image processing and analysis techniques. A wide range of software is available—from commercial to academic, special‐purpose to Swiss army knife, small to large—but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open‐source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open‐software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image‐processing algorithms. In this review, we use the ImageJ project as a case study of how open‐source software fosters its suites of software tools, making multitudes of image‐analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self‐influenced by coevolving projects within the ImageJ ecosystem. Mol. Reprod. Dev. 82: 518–529, 2015. © 2015 Wiley Periodicals, Inc.

Subfields: Biophysics, Media Technology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9ee46e72fd00004dcf92f4fc03854ae3851a3a2c

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Citations in the corpus

ImageJ2: ImageJ for the next generation of scientific image data (2017, background): “Two decades later, a plethora of such plugins and macros have been shared and published [20].”
An image J plugin for the high throughput image analysis of in vitro scratch wound healing assays (2020, methodology): “These packages have been previously used to manually count cells (selecting and tallying individual cells) and to assess wound closure (tracing the wound perimeter and calculating the percentage of closure) (23, 24).”
TrackMate: An open and extensible platform for single-particle tracking. (2017, methodology): “Track-Mate discovers these new modules automatically thanks to the SciJava annotation framework, which considerably simpliﬁes development.”