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An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

Author

Listed:
  • Joris Roels

    (VIB, Center for Inflammation Research
    Ghent University, Department of Applied Mathematics, Computer Science and Statistics)

  • Frank Vernaillen

    (VIB, Bioinformatics Core
    VIB, Bioimaging Core)

  • Anna Kremer

    (VIB, Center for Inflammation Research
    VIB, Bioimaging Core
    Ghent University, Department of Biomedical Molecular Biology)

  • Amanda Gonçalves

    (VIB, Center for Inflammation Research
    VIB, Bioimaging Core
    Ghent University, Department of Biomedical Molecular Biology)

  • Jan Aelterman

    (Ghent University/IMEC, Department of Telecommunications and Information Processing)

  • Hiêp Q. Luong

    (Ghent University/IMEC, Department of Telecommunications and Information Processing)

  • Bart Goossens

    (Ghent University/IMEC, Department of Telecommunications and Information Processing)

  • Wilfried Philips

    (Ghent University/IMEC, Department of Telecommunications and Information Processing)

  • Saskia Lippens

    (VIB, Center for Inflammation Research
    VIB, Bioimaging Core
    Ghent University, Department of Biomedical Molecular Biology)

  • Yvan Saeys

    (VIB, Center for Inflammation Research
    Ghent University, Department of Applied Mathematics, Computer Science and Statistics)

Abstract

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.

Suggested Citation

  • Joris Roels & Frank Vernaillen & Anna Kremer & Amanda Gonçalves & Jan Aelterman & Hiêp Q. Luong & Bart Goossens & Wilfried Philips & Saskia Lippens & Yvan Saeys, 2020. "An interactive ImageJ plugin for semi-automated image denoising in electron microscopy," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14529-0
    DOI: 10.1038/s41467-020-14529-0
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    Cited by:

    1. Hong, Junsung & Grimes, Jerren & Cox, Dalton & Barnett, Scott A., 2024. "Life testing of 10 cm × 10 cm fuel-electrode-supported solid oxide cells in reversible operation," Applied Energy, Elsevier, vol. 355(C).

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