Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

BioImageXD: an open, general-purpose and high-throughput image-processing platform


BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes. We demonstrate its performance in a study of integrin clustering in response to selected inhibitors.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: BioImageXD GUI.
Figure 2: Examples of visualizations created with BioImageXD.
Figure 3: Analyzing cell-surface receptor clustering and internalization.
Figure 4: Effects of inhibitors on integrin clustering, internalization and movement.


  1. Walter, T. et al. Visualization of image data from cells to organisms. Nat. Methods 7, S26–S41 (2010).

    Article  CAS  Google Scholar 

  2. Wilt, B.A. et al. Advances in light microscopy for neuroscience. Annu. Rev. Neurosci. 32, 435–506 (2009).

    Article  CAS  Google Scholar 

  3. Peng, H. Bioimage informatics: a new area of engineering biology. Bioinformatics 24, 1827–1836 (2008).

    Article  CAS  Google Scholar 

  4. Rueden, C.T. & Eliceiri, K.W. Visualization approaches for multidimensional biological image data. Biotechniques 43, 31–36 (2007).

    Article  Google Scholar 

  5. Rossner, M. & Yamada, K.M. What's in a picture? The temptation of image manipulation. J. Cell Biol. 166, 11–15 (2004).

    Article  CAS  Google Scholar 

  6. Anonymous. Microscopic marvels. Nature 459, 615 (2009).

  7. Bolte, S. & Cordelières, F.P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).

    Article  CAS  Google Scholar 

  8. French, A.P., Mills, S., Swarup, R., Bennett, M.J. & Pridmore, T.P. Colocalization of fluorescent markers in confocal microscope images of plant cells. Nat. Protoc. 3, 619–628 (2008).

    Article  CAS  Google Scholar 

  9. Costes, S.V. et al. Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys. J. 86, 3993–4003 (2004).

    Article  CAS  Google Scholar 

  10. van Steensel, B. et al. Partial colocalization of glucocorticoid and mineralocorticoid receptors in discrete compartments in nuclei of rat hippocampus neurons. J. Cell Sci. 109, 787–792 (1996).

    CAS  PubMed  Google Scholar 

  11. Fay, F.S., Taneja, K.L., Shenoy, S., Lifshitz, L. & Singer, R.H. Quantitative digital analysis of diffuse and concentrated nuclear distributions of nascent transcripts, SC35 and poly(A). Exp. Cell Res. 231, 27–37 (1997).

    Article  CAS  Google Scholar 

  12. O'Donoghue, S.I. et al. Visualizing biological data now and in the future. Nat. Methods 7, S2–S4 (2010).

    Article  CAS  Google Scholar 

  13. Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7, 690–696 (2006).

    Article  CAS  Google Scholar 

  14. Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727 (2010).

    Article  CAS  Google Scholar 

  15. Jaensch, S., Decker, M., Hyman, A.A. & Myers, E.W. Automated tracking and analysis of centrosomes in early Caenorhabditis elegans embryos. Bioinformatics 26, i13–i20 (2010).

    Article  CAS  Google Scholar 

  16. Walsh, E.G. et al. High content analysis to determine cytotoxicity of the antimicrobial peptide, melittin and selected structural analogs. Peptides 32, 1764–1773 (2011).

    Article  CAS  Google Scholar 

  17. Eliceiri, K.W. & Rueden, C. Tools for visualizing multidimensional images from living specimens. Photochem. Photobiol. 81, 1116–1122 (2005).

    Article  CAS  Google Scholar 

  18. Schroeder, W., Martin, K. & Lorensen, B. The Visualization Toolkit. An object-oriented approach to 3D graphics (Kitware, Inc., 2006).

  19. Yoo, T.S. et al. Engineering and algorithm design for an image processing API: a technical report on ITK—The Insight Toolkit. Proc. of Medicine Meets Virtual Reality. (ed. Westwood, J.) 586–592 (2002).

  20. Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).

    Article  CAS  Google Scholar 

  21. Joshi, A. et al. Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics 9, 69–84 (2011).

    Article  Google Scholar 

  22. Peng, H., Ruan, Z., Long, F., Simpson, J.K. & Myers, E.W. V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat. Biotechnol. 28, 348–353 (2010).

    Article  CAS  Google Scholar 

  23. Rueden, C., Eliceiri, K.W. & White, J.G. VisBio: a computational tool for visualization of multidimensional biological image data. Traffic 5, 411–417 (2004).

    Article  CAS  Google Scholar 

  24. Hynes, R.O. The extracellular matrix: not just pretty fibrils. Science 326, 1216–1219 (2009).

    Article  CAS  Google Scholar 

  25. Kawaguchi, S., Bergelson, J.M., Finberg, R.W. & Hemler, M.E. Integrin alpha 2 cytoplasmic domain deletion effects: loss of adhesive activity parallels ligand-independent recruitment into focal adhesions. Mol. Biol. Cell 5, 977–988 (1994).

    Article  CAS  Google Scholar 

  26. Meshel, A.S., Wei, Q., Adelstein, R.S. & Sheetz, M.P. Basic mechanism of three-dimensional collagen fibre transport by fibroblasts. Nat. Cell Biol. 7, 157–164 (2005).

    Article  CAS  Google Scholar 

  27. Upla, P. et al. Clustering induces a lateral redistribution of alpha 2 beta 1 integrin from membrane rafts to caveolae and subsequent protein kinase C-dependent internalization. Mol. Biol. Cell 15, 625–636 (2004).

    Article  CAS  Google Scholar 

  28. Karjalainen, M. et al. A Raft-derived, Pak1-regulated entry participates in alpha2beta1 integrin-dependent sorting to caveosomes. Mol. Biol. Cell 19, 2857–2869 (2008).

    Article  CAS  Google Scholar 

  29. Cromey, D.W. Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images. Sci. Eng. Ethics 16, 639–667 (2010).

    Article  Google Scholar 

  30. Pearson, H. The good, the bad and the ugly. Nature 447, 138–140 (2007).

    Article  CAS  Google Scholar 

  31. Gordon, A. et al. Single-cell quantification of molecules and rates using open-source microscope-based cytometry. Nat. Methods 4, 175–181 (2007).

    Article  CAS  Google Scholar 

  32. Goldberg, I.G. et al. The Open Microscopy Environment (OME) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005).

    Article  Google Scholar 

  33. Tchantchaleishvili, V. & Schmitto, J.D. Preparing a scientific manuscript in Linux: Today's possibilities and limitations. BMC Res. Notes 4, 434 (2011).

    Article  Google Scholar 

  34. de Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–696 (2011).

    Article  Google Scholar 

  35. Lamprecht, M.R., Sabatini, D.M. & Carpenter, A.E. CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 42, 71–75 (2007).

    Article  CAS  Google Scholar 

  36. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  Google Scholar 

  37. Nymalm, Y. et al. Jararhagin-derived RKKH peptides induce structural changes in alpha1I domain of human integrin alpha1beta1. J. Biol. Chem. 279, 7962–7970 (2004).

    Article  CAS  Google Scholar 

Download references


We acknowledge all our former programmers, scientists and collaborators for their contributions to the development of BioImageXD. This research was funded by the Academy of Finland (projects 127168 and 114727), the FinNano nanoscience research programme, the Finnish Funding Agency for Technology and Innovation, the Sigrid Juselius Foundation, the National Doctoral Programme in Informational and Structural Biology (ISB), and the European Union 7th framework program (MetaFight). We thank the staff and users of the core imaging facilities who provided feedback and test data: Turku BioImaging and the Cell Imaging Core of the Turku Centre for Biotechnology (University of Turku and Åbo Akademi University), Jyväskylä Imaging Facility (University of Jyväskylä), Light Microscopy Facility of the Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, and BioTec Zentrum, and Medical Theoretical Centre (Technical University of Dresden).

Author information

Authors and Affiliations



P.K. designed software and experiments, and wrote the manuscript; L.P. was head programmer; L.P. and J.P. programmed the software; P.K. and L.P. defined analysis protocols and did most analyses; S.T. and J.N. prepared samples, imaged and did some analyses; M.K. helped with software development; J.H. and V.M. designed scientific applications and software strategies; and D.J.W. supervised software development.

Corresponding author

Correspondence to Pasi Kankaanpää.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–7, Supplementary Methods (PDF 3897 kb)

Supplementary Data 1

Samples and instructions for testing clustering and internalization analysis. First see the readme PDF included in the zip file. (ZIP 5396 kb)

Supplementary Data 2

Samples and instructions for testing motion tracking. First see the readme PDF included in the zip file. (ZIP 1113 kb)

Supplementary Video 1

Screen capture video of 3D rendering with BioImageXD. This video illustrates how multichannel 3D renderings can be created, changed, adjusted and interacted with. First a volume rendering of one data channel is rotated and zoomed. Then a surface rendering module is loaded, and the same data channel is rendered with it. The iso-value for creating the surface is adjusted so that more image data are shown, and the rendering is then rotated and zoomed. A second data channel (yellow) is then selected for the surface rendering and adjusted so that maximum brightness is used. Finally the volume rendering module is reactivated, showing the first data channel (bluish). The two renderings, similar to those shown in Supplementary Figure 1, are then rotated and zoomed. Note that the movement in the video does not appear as smooth as it is in reality because the frame rate was decreased to free more processing power for the rendering. (MP4 4970 kb)

Supplementary Video 2

Screen capture video of creating an animation with BioImageXD. This video illustrates how a camera path animation can be created and encoded all the way to the finished video product. First a camera path track is loaded, and then a free-form camera path added to it. The 10-s timeline is enlarged, and positions of the camera path items are adjusted. Then the camera path itself (red line with green control nodes) is adjusted in the sample rendering panel. Finally the video file creation panel is activated, and video encoding settings are adjusted. After the user clicks OK, the software switches over to 3D mode and calculates every frame of the animation with the settings there, in this case a green surface rendering. After the video is encoded, the user closes BioImageXD and views the finished video file with an external player program. Note that movement in the video does not appear as smooth as it is in reality because the frame rate was decreased to free more processing power for the rendering. (MP4 1031 kb)

Supplementary Video 3

Example animation created with BioImageXD. This video shows two surface rendered data channels; the cell membrane is colored white and clusters of α2β1 integrins red. (Integrins are cell-surface receptors studied in the practical analysis example in this paper; they form clusters and are simultaneously internalized into the cell.) The animation was created with a camera path, and first shows the cell from the outside. The camera then goes inside the cell, where many of the integrin clusters can be seen internalized. The video was created from a three-dimensional confocal microscopy image obtained 30 min after integrin clustering was initiated. (MP4 1758 kb)

Supplementary Video 4

Screen capture video of segmenting receptor clusters with BioImageXD. This video illustrates how a procedure list for segmentation is set up and used to analyze α2β1integrin clusters. First, a list of four procedures is created: 'hybrid median 2D' (removes noise), 'dynamic thresholding' (separates the image into two classes, foreground and background, using locally calculated thresholds), 'object separation' (divides the foreground into separate objects and labels them; is capable of separating touching objects) and 'analyze segmented objects' (quantitatively analyzes segmented objects). Then the settings for each procedure are adjusted as applicable, by clicking their names and adjusting the controls that appear below. The procedure list is executed by clicking 'view result' (this takes a while, as the list includes heavy calculation). The results, which are similar to those shown in Supplementary Figure 3, are then inspected by the user, both the averages (highlighted in green) and the results for individual objects. The user chooses some objects from the objects list to identify them in the image. To try segmentation, see Supplementary Data 1. The quantitative analysis results can be saved into a csv file by clicking 'export statistics' (this is not shown on the video). Note that movement in the video does not appear as smooth as it is in reality because the frame rate was decreased to free more processing power for the segmentation. (MP4 675 kb)

Supplementary Video 5

Animation of motion tracking created with BioImageXD. This video shows semitransparent surface rendered clusters of α2β1 integrins in a cell. The video was created with keyframe animation of a 4D confocal microscopy dataset of a living cell imaged with 3-min time intervals. Motion tracking was performed on the integrin clusters, and one example motion path is rendered into the video. After the view is moved closer, time points for the entire duration of the path (48 min) are shown, and an integrin cluster can be seen moving along its tracked path. To try motion tracking, see Supplementary Data 2. (MP4 824 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kankaanpää, P., Paavolainen, L., Tiitta, S. et al. BioImageXD: an open, general-purpose and high-throughput image-processing platform. Nat Methods 9, 683–689 (2012).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing