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

Laboratory for Optical and Computational Instrumentation Center for Quantitative Cell Imaging University of Wisconsin at Madison Madison Wisconsin USA

Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany

Morgridge Institute for Research Madison Wisconsin USA

Zobrazit více v PubMed

Cardona A, Tomancak P. Current challenges in open‐source bioimage informatics. Nat Methods. 2012;9:661–665. PubMed

Guiet R, Burri O, Seitz A. Open source tools for biological image analysis In: Rebollo E, Bosch M, editors. Computer Optimized Microscopy: Methods and Protocols. Methods in Molecular Biology. New York, NY: Springer, 2019; p. 23–37. PubMed

Sahl SJ, Hell SW, Jakobs S. Fluorescence nanoscopy in cell biology. Nat Rev Mol Cell Biol. 2017;18:685–701. PubMed

Planchon TA, Gao L, Milkie DE, et al. Rapid three‐dimensional isotropic imaging of living cells using Bessel beam plane illumination. Nat Methods. 2011;8:417–423. PubMed PMC

Weber M, Mickoleit M, Huisken J. Light sheet microscopy. Methods Cell Biol. 2014;123:193–215. PubMed

Giepmans BNG, Adams SR, Ellisman MH, Tsien RY. The fluorescent toolbox for assessing protein location and function. Science. 2006;312:217–224. PubMed

Zipfel WR, Williams RM, Webb WW. Nonlinear magic: Multiphoton microscopy in the biosciences. Nat Biotechnol. 2003;21:1369–1377. PubMed

Eliceiri KW, Berthold MR, Goldberg IG, et al. Biological imaging software tools. Nat Methods. 2012;9:697–710. PubMed PMC

López‐Cano M, Fernández‐Dueñas V, Ciruela F. Proximity ligation assay image analysis protocol: Addressing receptor‐receptor interactions. Methods Mol Biol. 2019;2040:41–50. PubMed

Caldon CE, Burgess A. Label free, quantitative single‐cell fate tracking of time‐lapse movies. MethodsX. 2019;6:2468–2475. PubMed PMC

Grishagin IV. Automatic cell counting with ImageJ. Anal Biochem. 2015;473:63–65. PubMed

Pijuan J, Barceló C, Moreno DF, et al. In vitro cell migration, invasion, and adhesion assays: From cell imaging to data analysis. Front Cell Dev Biol. 2019;7:107. PubMed PMC

Della Mea V, Baroni GL, Pilutti D, Di Loreto C. SlideJ: An ImageJ plugin for automated processing of whole slide images. PLoS One. 2017;12:e0180540. PubMed PMC

Young K, Morrison H. Quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using ImageJ. J. Vis. Exp. 2018;136:57648. PubMed PMC

Preibisch S, Saalfeld S, Schindelin J, Tomancak P. Software for bead‐based registration of selective plane illumination microscopy data. Nat Methods. 2010;7:418–419. PubMed

Preibisch S, Amat F, Stamataki E, et al. Efficient Bayesian‐based multiview deconvolution. Nat Methods. 2014;11:645–648. PubMed PMC

Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Med Image Anal. 2016;33:170–175. PubMed PMC

Teigen LM, Kuchnia AJ, Nagel E, et al. Impact of software selection and ImageJ tutorial corrigendum on skeletal muscle measures at the third lumbar vertebra on computed Tomography scans in clinical populations. JPEN J Parenter Enteral Nutr. 2018;42:933–941. PubMed

Selvan AN, Cole LM, Spackman L, Naylor S, Wright C. Hierarchical cluster analysis to aid diagnostic image data visualization of MS and other medical imaging modalities. Methods Mol. Biol. 2017;1618:95–123. PubMed

Brookes SJ. Using ImageJ (Fiji) to analyze and present X‐ray CT images of enamel. Methods Mol. Biol. 2019;1922:267–291. PubMed

Domínguez C, Heras J, Pascual V. IJ‐OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine. Comput Biol Med. 2017;84:189–194. PubMed

Meijering E, Cappellen G. van quantitative biological image analysis In: Shorte SL, Frischknecht F, editors. Imaging cellular and molecular biological functions. Principles and practice. Berlin, Heidelberg: Springer, 2007; p. 45–70.

Schneider CA, Rasband WS, Eliceiri KW. NIH image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–675. PubMed PMC

Schindelin J, Arganda‐Carreras I, Frise E, et al. Fiji: An open‐source platform for biological‐image analysis. Nat Methods. 2012;9:676–682. PubMed PMC

Anon Introduction to Fiji. Available from http://imagej.github.io/presentations/fiji-introduction/#/2

Pietzsch T, Preibisch S, Tomančák P, Saalfeld S. ImgLib2—Generic image processing in Java. Bioinformatics. 2012;28:3009–3011. PubMed PMC

Rueden CT, Schindelin J, Hiner MC, et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinf. 2017;18:529. PubMed PMC

Schindelin J. The ImageJ ecosystem: An open platform for biomedical image analysis; 2015. Available from http://onlinelibrary.wiley.com/doi/10.1002/mrd.22489/full PubMed DOI PMC

Arena ET, Rueden CT, Hiner MC, Wang S, Yuan M, Eliceiri KW. Quantitating the cell: Turning images into numbers with ImageJ. WIREs Dev Biol. 2017;6:e260. PubMed

Pietzsch T, Saalfeld S, Preibisch S, Tomancak P. BigDataViewer: Visualization and processing for large image data sets. Nat Methods. 2015;12:481–483. PubMed

Hörl D, Rojas Rusak F, Preusser F, et al. BigStitcher: Reconstructing high‐resolution image datasets of cleared and expanded samples. Nat Methods. 2019;16:870–874. PubMed

Bogovic JA Robust registration of calcium images by learned contrast synthesis. 2016. Available from https://ieeexplore.ieee.org/document/7493463

Linkert M, Rueden CT, Allan C, et al. Metadata matters: Access to image data in the real world. J Cell Biol. 2010;189:777–782. PubMed PMC

Royer LA, Weigert M, Günther U, et al. ClearVolume: Open‐source live 3D visualization for light‐sheet microscopy. Nat Methods. 2015;12:480–481. PubMed

Weigert M, Schmidt U, Boothe T, et al. Content‐aware image restoration: Pushing the limits of fluorescence microscopy. Nat Methods. 2018;15:1090–1097. PubMed

Gómez‐de‐Mariscal E, García‐López‐de‐Haro C, Donati L, Unser M, Muñoz‐Barrutia A, Sage D. DeepImageJ: A user‐friendly plugin to run deep learning models in ImageJ. bioRxiv:799270; 2019. PubMed

Fernandez R, Moisy C. Fijiyama: A registration tool for 3D multimodal time‐lapse imaging. Bioinformatics. 2020;btaa846 10.1093/bioinformatics/btaa846 PubMed DOI

Gao D, Barber PR, Chacko JV, Sagar MAK, Rueden CT, Grislis AR, Hiner MC, Eliceiri KW (2020) FLIMJ: An open‐source ImageJ toolkit for fluorescence lifetime image data analysis. bioRxiv:2020.08.17.253625. PubMed PMC

Anon FLIMJ . ImageJ. Available from https://imagej.net/FLIMJ

Wolff C Multi‐view light‐sheet imaging and tracking with the MaMuT software reveals the cell lineage of a direct developing arthropod limb; 2018. Available from https://elifesciences.org/articles/34410 PubMed PMC

Anon mastodon‐sc/mastodon . Mastodon Science; 2020. Available from https://github.com/mastodon-sc/mastodon

Hiner MC, Rueden CT, Eliceiri KW. SCIFIO: An extensible framework to support scientific image formats. BMC Bioinf. 2016;17:521. PubMed PMC

Anon SciView . ImageJ. Available from https://imagej.net/SciView

Günther U, Harrington KIS (2020) Tales from the Trenches: Developing SciView, a new 3D viewer for the ImageJ community. ArXiv200411897 Cs. Available from http://arxiv.org/abs/2004.11897

Arganda‐Carreras I, Kaynig V, Rueden C, et al. Trainable Weka segmentation: A machine learning tool for microscopy pixel classification. Bioinf Oxf Engl. 2017;33:2424–2426. PubMed

Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: An update. ACM SIGKDD Explor Newslett. 2009;11:10–18.

Cardona A, Saalfeld S, Schindelin J, et al. TrakEM2 software for neural circuit reconstruction. PLoS One. 2012;7:e38011. PubMed PMC

Martiel J‐L, Leal A, Kurzawa L, et al. Measurement of cell traction forces with ImageJ. Methods Cell Biol. 2015;125:269–287. PubMed

Brazill JM, Zhu Y, Li C, Zhai RG. Quantitative cell biology of neurodegeneration in drosophila through unbiased analysis of fluorescently tagged proteins using ImageJ. J. Vis. Exp. 2018;138:58041. PubMed PMC

Patel A, Li Z, Canete P, et al. AxonTracer: A novel ImageJ plugin for automated quantification of axon regeneration in spinal cord tissue. BMC Neurosci. 2018;19:8. PubMed PMC

Hayes JA, Papagiakoumou E, Ruffault P‐L, Emiliani V, Fortin G. Computer‐aided neurophysiology and imaging with open‐source PhysImage. J Neurophysiol. 2018;120:23–36. PubMed

Lormand C, Zellmer GF, Németh K, et al. Weka trainable segmentation plugin in ImageJ: A semi‐automatic tool applied to crystal size distributions of Microlites in volcanic rocks. Microsc Microanal Off J Microsc Soc Am Microbeam Anal Soc Microsc Soc Can. 2018;24:667–675. PubMed

Collins KA, Kielkopf JF, Stassun KG, Hessman FV. ASTROIMAGEJ: Image processing and photometric extraction for ultra‐precise astronomical light curves. Astron J. 2017;153:77.

Voras ZE (2017) Binding Medium Alteration and its Effect on Fine Art Paintings as Observed by Surface Analysis. Available from https://search.proquest.com/docview/1972679309/abstract/5F652F1A30D44387PQ/1

Kapitany K, Somogyi A, Barsi A. Inspection of a medieval WOOD sculpture using computer TOMOGRAPHY. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016;XLI‐B5:287–291.

Williams C, Wu Y, Bowers DF. ImageJ analysis of dentin tubule distribution in human teeth. Tissue Cell. 2015;47:343–348. PubMed

Rueden CT, Ackerman J, Arena ET, et al. Scientific community image forum: A discussion forum for scientific image software. PLoS Biol. 2019;17:e3000340. PubMed PMC

Anon How to contribute to an existing plugin or library. ImageJ. Available from https://imagej.net/How_to_contribute_to_an_existing_plugin_or_library

Abadi M. TensorFlow: Large‐scale machine learning on heterogeneous distributed systems; 2016. Available from https://arxiv.org/abs/1603.04467

Anon CSBDeep . ImageJ. Available from https://imagej.net/CSBDeep

Anon CSBDeep . CSBDeep. Available from https://csbdeep.bioimagecomputing.com/tools

Čepa M. Segmentation of total cell area in brightfield microscopy images. Methods Protoc. 2018;1:43. PubMed PMC

Miller KE, Liu X‐A, Puthanveettil SV. Automated measurement of fast mitochondrial transport in neurons. Front Cell Neurosci. 2015;9:435. PubMed PMC

Murtin C, Frindel C, Rousseau D, Ito K. Image processing for precise three‐dimensional registration and stitching of thick high‐resolution laser‐scanning microscopy image stacks. Comput Biol Med. 2018;92:22–41. PubMed

Saalfeld S. Computational methods for stitching, alignment, and artifact correction of serial section data. Methods Cell Biol. 2019;152:261–276. PubMed

Anon SciJava. ImageJ. Available from https://imagej.net/SciJava

Chenouard N, Smal I, de Chaumont F, et al. Objective comparison of particle tracking methods. Nat Methods. 2014;11:281–289. PubMed PMC

Meijering E. Cell segmentation: 50 years down the road [life sciences]. IEEE Signal Process Mag. 2012;29:140–145.

Jaqaman K, Loerke D, Mettlen M, et al. Robust single particle tracking in live cell time‐lapse sequences. Nat Methods. 2008;5:695–702. PubMed PMC

Tinevez J‐Y, Perry N, Schindelin J, et al. TrackMate: An open and extensible platform for single‐particle tracking. Methods. 2017;115:80–90. PubMed

Cardona A TrakEM2 software for neural circuit reconstruction; 2012. Available from 10.1371/journal.pone.0038011 PubMed DOI PMC

Anon Fijiyama . ImageJ. Available from https://imagej.net/Fijiyama

Schmid B A high‐level 3D visualization API for Java and ImageJ. 2010. Available from http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-274 PubMed DOI PMC

Anon Introduction into Macro Programming. ImageJ. Available from https://imagej.net/Introduction_into_Macro_Programming

Mutterer J, Rasband W. ImageJ Macro Language Programmer's Reference Guide v1.46d.:45.

Anon ImageJ. Available from. https://imagej.nih.gov/ij/macros/

Anon Scripting . ImageJ. Available from https://imagej.net/Scripting

Anon BigStitcher . ImageJ. Available from https://imagej.net/BigStitcher

Anon The bh TCSPC Handbook 8th ed. Becker Hickl GmbH. Available from. https://www.becker-hickl.com/literature/handbooks/the-bh-tcspc-handbook/

Berezin MY, Achilefu S. Fluorescence lifetime measurements and biological imaging. Chem Rev. 2010;110:2641–2684. PubMed PMC

Lakowicz JR, Szmacinski H, Nowaczyk K, Johnson ML. Fluorescence lifetime imaging of free and protein‐bound NADH. Proc Natl Acad Sci U S A. 1992;89:1271–1275. PubMed PMC

Bird DK, Eliceiri KW, Fan C‐H, White JG. Simultaneous two‐photon spectral and lifetime fluorescence microscopy. Appl Optics. 2004;43:5173–5182. PubMed

Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A primer for the epidemiologist. Am J Epidemiol. 2019;188:2222–2239. PubMed

Ballard DH, Brown CM. Computer Vision. Englewood Cliffs, NJ: Prenice‐Hall, 1982.

Yang SJ Assessing microscope image focus quality with deep learning; 2018. Available from 10.1186/s12859-018-2087-4 PubMed DOI PMC

Anon OpenCV . ImageJ. Available from https://imagej.net/OpenCV

Anon imagej/imagej‐opencv. ImageJ; 2020. Available from https://github.com/imagej/imagej-opencv

Zhang YC, Kagen AC. Machine learning Interface for medical image analysis. J Digit Imaging. 2017;30:615–621. PubMed PMC

Ahn JM, Kim S, Ahn K‐S, Cho S‐H, Lee KB, Kim US. A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PLoS One. 2018;13:e0207982. PubMed PMC

Cai S, Tian Y, Lui H, Zeng H, Wu Y, Chen G. Dense‐UNet: A novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant Imaging Med Surg. 2020;10:1275–1285. PubMed PMC

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444. PubMed

Krull A, Buchholz T‐O, Jug F. Noise2Void ‐ learning denoising from single noisy images. Paper presented at: Conference on Computer Vision and Pattern Recognition (CVPR); 2019; pp. 2129–2137.

Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Z Für Med Phys. 2019;29:86–101. PubMed

Wang H, Rivenson Y, Jin Y, et al. Deep learning enables cross‐modality super‐resolution in fluorescence microscopy. Nat Methods. 2019;16:103–110. PubMed PMC

Rivenson Y, Wang H, Wei Z, et al. Virtual histological staining of unlabelled tissue‐autofluorescence images via deep learning. Nat Biomed Eng. 2019;3:466–477. PubMed

Falk T, Mai D, Bensch R, et al. Author correction: U‐net: Deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16:351. PubMed

Buchholz T‐O, Prakash M, Krull A, Jug F (2020) DenoiSeg: Joint denoising and segmentation. ArXiv200502987 Cs . Available from http://arxiv.org/abs/2005.02987

Schmidt U, Weigert M, Broaddus C, Myers G (2018) Cell detection with star‐convex polygons. ArXiv180603535 Cs 11071:265–273.

McQuilken M, Mehta SB, Verma A, Harris G, Oldenbourg R, Gladfelter AS. Polarized fluorescence microscopy to study cytoskeleton assembly and organization in live cells. Curr Protoc Cell Biol. 2015;67:4.29.1–4.29.13. PubMed PMC

Pitrone PG, Schindelin J, Stuyvenberg L, et al. OpenSPIM: An open‐access light‐sheet microscopy platform. Nat Methods. 2013;10:598–599. PubMed PMC

Anon ImageJ Ops. ImageJ. Available from https://imagej.net/ImageJ_Ops

High efficiency of laser energy conversion with cavity pressure acceleration

Assessment of the Concentration of Transforming Growth Factor Beta 1-3 in Degenerated Intervertebral Discs of the Lumbosacral Region of the Spine