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

• Published in: Protein science Vol. 30; no. 1; pp. 234 - 249
• Main Authors: Schroeder, Alexandra B., Dobson, Ellen T. A., Rueden, Curtis T., Tomancak, Pavel, Jug, Florian, Eliceiri, Kevin W.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-01-2021; Wiley Subscription Services, Inc
• Subjects: Adaptation; Artificial Intelligence; Collaboration; Complexity; Computer programs; Data acquisition; Datasets; Ecosystems; Environmental changes; Fiji; Image acquisition; Image analysis; Image enhancement; Image processing; Image Processing, Computer-Assisted; Image segmentation; ImageJ; imaging; Interoperability; microscopy; Open source software; Software; Tools for Protein Science; Visualization; ImageJ; microscopy; Fiji; open source software; image analysis; imaging
• ISSN: 0961-8368; 1469-896X
• DOI: 10.1002/pro.3993

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.
Highlights
ImageJ is an open‐source image analysis software with a large, diverse user base. ImageJ has several components and distributions such that we refer to the entirety as the ImageJ ecosystem. Recent developments have adapted to the needs of users and the demands of increasingly large, complex biological datasets accompanying technological advancements in imaging. This review highlights several new tools developed in the ImageJ ecosystem to address needs for improved visualization and analysis of biological features.