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The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis
AB. Schroeder, ETA. Dobson, CT. Rueden, P. Tomancak, F. Jug, KW. Eliceiri
Language English Country United States
Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
Grant support
P41-GM135019
NIGMS NIH HHS - United States
NLK
Free Medical Journals
from 1992 to 1 year ago
PubMed Central
from 1992 to 1 year ago
Europe PubMed Central
from 1992 to 1 year ago
Medline Complete (EBSCOhost)
from 2010-01-01 to 1 year ago
Wiley Free Content
from 1996 to 1 year ago
PubMed
33166005
DOI
10.1002/pro.3993
Knihovny.cz E-resources
- MeSH
- Image Processing, Computer-Assisted * MeSH
- Software * MeSH
- Artificial Intelligence * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
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.
Center for Systems Biology Dresden Dresden Germany
Department of Biomedical Engineering University of Wisconsin at Madison Madison Wisconsin USA
Department of Medical Physics University of Wisconsin at Madison Madison Wisconsin USA
Fondazione Human Technopole Milan Italy
IT4Innovations VŠB Technical University of Ostrava Ostrava Czech Republic
Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany
References provided by Crossref.org
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