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CLIJ: GPU-accelerated image processing for everyone

Nature Methods volume 17, pages 5–6 (2020)Cite this article

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To the editor — Modern microscopy generates staggering amounts of multidimensional image data that place increasing demands on processing flexibility and efficiency. One way to speed up image processing is to exploit the parallel processing capabilities of graphics processing units (GPU).

Recently, GPU-acceleration was used in specific image-processing tasks such as reconstruction1,2, image quality determination3, image restoration4, segmentation5 or visualization6. However, in such tools, GPU code is fulfilling one specific purpose and is not intended to be reused in other contexts. By contrast, most common image processing tasks are solved by building flexible workflows consisting of simple operations in widely used tools such as ImageJ7 and Fiji8. Most of these operations were however programmed at a time when GPUs were not commonly used for general-purpose processing. Therefore, typical workflows consisting of core ImageJ operations do not take advantage of GPUs. To address this issue, we developed a flexible and reusable platform for GPU-acceleration in Fiji.

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Fig. 1: CLIJ performance overview.

Data availability

All data to support our findings are available through the links listed in Supplementary Table 2. Further details can be found in the Reporting Summary.

Code availability

CLIJ is available as Supplementary Software 2. The fully open source code is available through the links listed in Supplementary Table 2. Updated versions of the software can also be found at https://clij.github.io. Further details can be found in the Reporting Summary.

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Acknowledgements

We would like to thank everybody who helped developing and testing CLIJ. In particular thanks goes to A. Herbert (University of Sussex), B. C. Vellutini (MPI-CBG), C. Rueden (UW-Madison LOCI), D. Krunic (DKFZ), D. J. White (GE), G. G. Martins (IGC), S. Culley (LMCB MRC), G. Cardone (MPI Biochem), J. Brocher (Biovoxxel), J. Girstmair (MPI-CBG), J. Gluch (Fraunhofer IKTS), K. Miura, L. Thomas (Acquifer), N. Stuurman (UCSF), P. Haub, P. Rajasekhar (Monash University), T. Pietzsch (MPI-CBG), W. Adams (VU Biophotonics). R.H. was supported by the German Federal Ministry of Research and Education (BMBF) under the code 031L0044 (Sysbio II) and D.S. received support from the German Research Foundation (DFG) under the code JU3110/1-1. P.T. was supported by the European Regional Development Fund in the IT4Innovations national supercomputing center-path to exascale project, project number CZ.02.1.01/0.0/0.0/16_013/0001791 within the Operational Programme Research, Development and Education.

Author information

Author notes

  1. Peter Steinbach

    Present address: Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany

  2. These authors contributed equally: Robert Haase, Loic A. Royer.

Authors and Affiliations

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

    Robert Haase, Peter Steinbach, Deborah Schmidt, Alexandr Dibrov, Uwe Schmidt, Martin Weigert, Nicola Maghelli, Pavel Tomancak, Florian Jug & Eugene W. Myers

  2. Center for Systems Biology Dresden, Dresden, Germany

    Robert Haase, Peter Steinbach, Deborah Schmidt, Alexandr Dibrov, Uwe Schmidt, Martin Weigert, Nicola Maghelli, Pavel Tomancak, Florian Jug & Eugene W. Myers

  3. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Loic A. Royer

  4. IT4Innovations, VŠB - Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Pavel Tomancak

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  1. Robert Haase
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Contributions

R.H. and L.A.R initiated the research. R.H., L.A.R., P.S, D.S., A.D., U.S. and M.W. wrote the source code of CLIJ. R.H. and N.M. performed the image acquisition of the example data. E.W.M. supervised the project. R.H., L.A.R., U.S., M.W., P.T. and F.J. wrote the manuscript with input from all co-authors.

Corresponding authors

Correspondence to Robert Haase or Loic A. Royer.

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Competing interests

The authors declare no competing interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–7, Supplementary Tables 1 and 2, and Supplementary Notes 1 and 2.

Reporting Summary

Supplementary Video 1

Results from processed time-lapse for benchmarking workflow. A video showing a developing Drosophila embryo in maximum projection and cylinder maximum projection with marked spot detection and spot count plotted over time.

Supplementary Software 1

CLIJ usage examples: Scripts demonstrating CLIJ in various ImageJ/Fiji compatible programming languages.

Supplementary Software 2

Reviewed version of CLIJ software.

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Haase, R., Royer, L.A., Steinbach, P. et al. CLIJ: GPU-accelerated image processing for everyone. Nat Methods 17, 5–6 (2020). https://doi.org/10.1038/s41592-019-0650-1

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