IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-22518-0.html
   My bibliography  Save this article

Democratising deep learning for microscopy with ZeroCostDL4Mic

Author

Listed:
  • Lucas von Chamier

    (University College London)

  • Romain F. Laine

    (University College London
    The Francis Crick Institute)

  • Johanna Jukkala

    (University of Turku and Åbo Akademi University
    Åbo Akademi University)

  • Christoph Spahn

    (Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt)

  • Daniel Krentzel

    (The Francis Crick Institute
    Imperial College London)

  • Elias Nehme

    (Technion—Israel Institute of Technology
    Technion—Israel Institute of Technology)

  • Martina Lerche

    (University of Turku and Åbo Akademi University)

  • Sara Hernández-Pérez

    (University of Turku and Åbo Akademi University
    Institute of Biomedicine, and MediCity Research Laboratories, University of Turku)

  • Pieta K. Mattila

    (University of Turku and Åbo Akademi University
    Institute of Biomedicine, and MediCity Research Laboratories, University of Turku)

  • Eleni Karinou

    (Newcastle University)

  • Séamus Holden

    (Newcastle University)

  • Ahmet Can Solak

    (Chan Zuckerberg Biohub)

  • Alexander Krull

    (Center for Systems Biology Dresden (CSBD)
    Max Planck Institute for Molecular Cell Biology and Genetics
    Max Planck Institute for Physics of Complex Systems)

  • Tim-Oliver Buchholz

    (Center for Systems Biology Dresden (CSBD)
    Max Planck Institute for Molecular Cell Biology and Genetics)

  • Martin L. Jones

    (The Francis Crick Institute)

  • Loïc A. Royer

    (Chan Zuckerberg Biohub)

  • Christophe Leterrier

    (Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto)

  • Yoav Shechtman

    (Technion—Israel Institute of Technology)

  • Florian Jug

    (Center for Systems Biology Dresden (CSBD)
    Max Planck Institute for Molecular Cell Biology and Genetics
    Fondatione Human Technopole)

  • Mike Heilemann

    (Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt)

  • Guillaume Jacquemet

    (University of Turku and Åbo Akademi University
    Åbo Akademi University)

  • Ricardo Henriques

    (University College London
    The Francis Crick Institute
    Instituto Gulbenkian de Ciência)

Abstract

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.

Suggested Citation

  • Lucas von Chamier & Romain F. Laine & Johanna Jukkala & Christoph Spahn & Daniel Krentzel & Elias Nehme & Martina Lerche & Sara Hernández-Pérez & Pieta K. Mattila & Eleni Karinou & Séamus Holden & Ahm, 2021. "Democratising deep learning for microscopy with ZeroCostDL4Mic," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22518-0
    DOI: 10.1038/s41467-021-22518-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-22518-0
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-22518-0?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. Shivesh Chaudhary & Sihoon Moon & Hang Lu, 2022. "Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Francesca Pennacchietti & Jonatan Alvelid & Rodrigo A. Morales & Martina Damenti & Dirk Ollech & Olena S. Oliinyk & Daria M. Shcherbakova & Eduardo J. Villablanca & Vladislav V. Verkhusha & Ilaria Tes, 2023. "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Timo Kuhn & Amit N. Landge & David Mörsdorf & Jonas Coßmann & Johanna Gerstenecker & Daniel Čapek & Patrick Müller & J. Christof M. Gebhardt, 2022. "Single-molecule tracking of Nodal and Lefty in live zebrafish embryos supports hindered diffusion model," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Simone Cauzzo & Ester Bruno & David Boulet & Paul Nazac & Miriam Basile & Alejandro Luis Callara & Federico Tozzi & Arti Ahluwalia & Chiara Magliaro & Lydia Danglot & Nicola Vanello, 2024. "A modular framework for multi-scale tissue imaging and neuronal segmentation," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Kaarjel K. Narayanasamy & Johanna V. Rahm & Siddharth Tourani & Mike Heilemann, 2022. "Fast DNA-PAINT imaging using a deep neural network," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Matthias Griebel & Dennis Segebarth & Nikolai Stein & Nina Schukraft & Philip Tovote & Robert Blum & Christoph M. Flath, 2023. "Deep learning-enabled segmentation of ambiguous bioimages with deepflash2," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Joshua J. Rennick & Cameron J. Nowell & Colin W. Pouton & Angus P. R. Johnston, 2022. "Resolving subcellular pH with a quantitative fluorescent lifetime biosensor," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Shijia Zhu & Naoto Kubota & Shidan Wang & Tao Wang & Guanghua Xiao & Yujin Hoshida, 2024. "STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    9. Rui Chen & Jiasu Xu & Boqian Wang & Yi Ding & Aynur Abdulla & Yiyang Li & Lai Jiang & Xianting Ding, 2024. "SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    10. Sara Rojas-Vázquez & Beatriz Lozano-Torres & Alba García-Fernández & Irene Galiana & Ana Perez-Villalba & Pablo Martí-Rodrigo & M. José Palop & Marcia Domínguez & Mar Orzáez & Félix Sancenón & Juan F., 2024. "A renal clearable fluorogenic probe for in vivo β-galactosidase activity detection during aging and senolysis," Nature Communications, Nature, vol. 15(1), pages 1-16, 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:12:y:2021:i:1:d:10.1038_s41467-021-22518-0. 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.