IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-14529-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-14529-0
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-020-14529-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).
    2. Andreas Müller & Nikolai Klena & Song Pang & Leticia Elizabeth Galicia Garcia & Oleksandra Topcheva & Solange Aurrecoechea Duran & Davud Sulaymankhil & Monika Seliskar & Hassan Mziaut & Eyke Schöniger, 2024. "Structure, interaction and nervous connectivity of beta cell primary cilia," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14529-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.