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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.

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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.


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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.

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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)

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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).

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