Fiji: an open-source platform for biological-image analysis

Journal name:
Nature Methods
Year published:
Published online


Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

At a glance


  1. Fiji as a high-powered distribution of ImageJ.
    Figure 1: Fiji as a high-powered distribution of ImageJ.

    (a) Screenshot of Fiji's main window, which is identical to that of ImageJ (top). Biology researchers can interact with multidimensional image data in Fiji's point-and-click interface, which is identical to that of ImageJ. Bioinformaticians can construct image-processing pipelines with scripting languages using the Script Editor plugin (screenshot is shown). Software engineers can use powerful software libraries such as ImgLib (schematic diagram of ImgLib design is shown) to transform mathematical formulations of computer science algorithms to functional programs (mathematical formulation of difference of Gaussian blob detector). (b) The Fiji updater workflow. Researchers who created a new plugin can contribute their code to the primary update server in Dresden, Germany, where their contribution will be commented on, enhanced and maintained for the long term by a group of core Fiji developers. Alternatively, anyone can offer their plugins directly through their own secondary update site. Users can freely customize their Fiji installation by choosing what to download.

  2. Scripting and ImgLib.
    Figure 2: Scripting and ImgLib.

    (a) Screenshot of 4D Viewer plugin with orthogonal view of the 3D image of Drosophila melanogaster first instar brain (left), in which cortex and neuropile glia are green, labeled by Nirvana-Gal4 and UASmcd8GFP, surface glial cells are red, labeled with anti-repo antibody and all nuclei are blue, labeled with Sytox. Red spheres mark the surface glial cells detected using a simple Jython script shown on the right. The script opens a 3D RGB image (line 5) and automatically counts red surface glial cells using the DoG detector (line 9), applying constraints for cell size and labeling intensity (lines 3 and 4). These constraints are expressed as DoG sigma parameters (lines 7 and 8) by extracting image dimensions from metadata (line 6). Cell count is printed in the dialog box (line 10), and cells are subsequently displayed in the 4D Viewer as red spheres of fixed diameter (lines 13–16). (bj) Output of ImgLib algorithms MSER (d,g,j) and DoG (c,f,i) for one (c,d), two (f,g) and three (i,j) dimensions. The 3D input image was a confocal stack of C. elegans expressing a nuclear marker (h); a slice from the stack was used as the 2D input image (e) (scale bar, 10 μm), and a line segment from the slice was used as the 1D input image (b). MSER and DoG were run on all input images without changing the code. Nested MSER regions representing competing segmentation hypotheses for the nuclei are colored green, red, blue and magenta.

  3. Fiji projects.
    Figure 3: Fiji projects.

    (ae) Stitching plugin for globally optimal registration of tiled 3D confocal images. The 3D confocal stacks of D. melanogaster first instar larval nervous system (a) were registered using phase correlation with global optimization (b) and visualized in the Fiji 4D Viewer (c). Four labeled neurons (color coded in 4D Viewer (d)) were segmented using a manual segmentation plugin (segmentation editor) and their volumes were measured (e). (fj) Globally optimal reconstruction of large ssTEM mosaics using TrakEM2 plugin. Schematic of the ssTEM mosaic; each square is an individual image tile, and independent sections are color-coded (f). Screenshot of a video visualizing the progress of global optimization for a single section. SIFT features (SIFT), and residual error signifying displacement of corresponding SIFT features at the current iteration (error) are shown (g). Dual-color overlay of two registered consecutive sections showing the entire hemisphere of the larval brain (h). Scale bar, 10 μm. Axonal profiles in a small part of a single section in the ssTEM data set were manually segmented using TrakEM2. Each profile is labeled with a different color (i). Scale bar, 0.5 μm. Relationship between numbers of presynaptic partners and postsynaptic sites extracted manually with TrakEM2 from a micro-cube of the registered data (j). (ko) Plugin suite for processing of multiview SPIM data. Schematic representation of multiview (4D) SPIM imaging showing 3D stacks of the same specimen acquired from different angles (k). Progress of the global optimization of multiview SPIM acquisition of a D. melanogaster embryo (l). Corresponding geometric bead descriptors are colored according to their residual displacement at the current iteration of the optimizer. The resulting reconstructed embryo at the 12th (top) and 13th (bottom) nuclear division cycle shown as a 3D rendering in Fiji's 3D viewer (m). Scale bar, 50 μm. Results of the DoG segmentation of the nuclei marked with His-YFP (n); same stages as in m. Each nucleus is marked with a different color. Quantification of the nuclear counts at the 12th and 13th nuclear division in the embryo shown in m and n (o).

  4. Fiji usage.
    Figure 4: Fiji usage.

    (a) A chart showing the number of unique visitors to the Fiji wiki per month over the last three years. (b) World map with locations of computers that updated Fiji between 20 March 2012 and 27 March 2012.


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Author information


  1. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.

    • Johannes Schindelin,
    • Tobias Pietzsch,
    • Stephan Preibisch,
    • Stephan Saalfeld,
    • Daniel James White &
    • Pavel Tomancak
  2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Ignacio Arganda-Carreras
  3. Department of Genome Dynamics, Berkeley Drosophila Genome Project, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

    • Erwin Frise
  4. Department of Computer Science of the Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.

    • Verena Kaynig
  5. Institute of Neuroinformatics of the University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.

    • Mark Longair &
    • Albert Cardona
  6. Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.

    • Curtis Rueden &
    • Kevin Eliceiri
  7. Department of Neurobiology and Genetics, University of Wurzburg, Wurzburg, Germany.

    • Benjamin Schmid
  8. Institut Pasteur, Imagopole, La plate-forme d'imagerie dynamique, Paris, France.

    • Jean-Yves Tinevez
  9. Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, USA.

    • Volker Hartenstein
  10. Present addresses: Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA (J.S.), Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany (B.S.) and Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA (S.P. and A.C.).

    • Johannes Schindelin,
    • Stephan Preibisch,
    • Benjamin Schmid &
    • Albert Cardona

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Text and Figures (1M)

    Supplementary Figures 1–3 and Supplementary Table 1


  1. Supplementary Video 1 (11M)

    Visualization of Fiji development. The video, produced using 'gource' tool in Git, visualizes the changes to Fiji source code repository from 15 March 2009 to 16 May 2009. The class hierarchy is visualized as a dynamic tree, the developers are flying pawns that extend rays to classes that they newly created or into which they introduced changes. Between 23 March and 3 April 2009 there was a Fiji hackathon in Dresden, Germany, marked by increased developer activity that carries over the period after the hackathon ended, the 'hackathon effect'.

  2. Supplementary Video 2 (16M)

    Visualization of SIFT-mediated stitching of large ssTEM mosaics. The ventral nerve cord of Drosophila first instar larva was sectioned and imaged in electron microscope as a series of overlapping image tiles. The video visualizes the process of reconstruction of such large section series on seven exemplary sections. The corresponding SIFT features that connect images within section and across section are shown as green dots, the residual error of their displacement at a given iteration of the global optimizer is shown as cyan line (iteration number and minimal, average and maximal error are shown in lower left corner). The global optimization proceeds section by section and at each step distributes the registration error equally across the increasing set of tiles. To emphasize the visualization effect all tiles within section are initially placed at the same location discarding their known configuration within section.

  3. Supplementary Video 3 (7M)

    Visualization of bead-based registration of multiview microscopy scan of Drosophila embryo. Drosophila embryo expressing His-YFP marker has been imaged in a spinning disc confocal microscope from 18 different angles improvising rotation using custom made sample chamber. The video visualizes the global optimization that is using local geometric bead descriptor matches to recover the shape of the embryo specimen. The bead descriptors (representing constellations of sub-resolution fluorescent beads added to the rigid agarose medium in which the embryo was mounted) are colored according to their displacement at each iteration of the optimizer (red, maximum displacement; green, minimum displacement). The nuclei of the embryo specimen are shown in grey. The displacement at each iteration averaged across all descriptors is shown in the lower left corner.

  4. Supplementary Video 4 (14M)

    Segmentation and tracking of nuclei in Drosophila embryo. Cellular blastoderm stage Drosophila embryo expressing His-YFP marker in all cells was imaged from five angles using SPIM throughout gastrulation. The video shows a result of segmentation and tracking algorithm that follows the movements of cells through the gastrulation process. The nuclei are colored according to the angle at which they were detected.

Additional data