IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007673.html
   My bibliography  Save this article

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Author

Listed:
  • Jean-Baptiste Lugagne
  • Haonan Lin
  • Mary J Dunlop

Abstract

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking

Suggested Citation

  • Jean-Baptiste Lugagne & Haonan Lin & Mary J Dunlop, 2020. "DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-18, April.
    • Handle: RePEc:plo:pcbi00:1007673
    DOI: 10.1371/journal.pcbi.1007673
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007673
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007673&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1007673?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
    ---><---

    References listed on IDEAS

    as
    1. Remy Chait & Jakob Ruess & Tobias Bergmiller & Gašper Tkačik & Călin C. Guet, 2017. "Shaping bacterial population behavior through computer-interfaced control of individual cells," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. François Bertaux & Sebastián Sosa-Carrillo & Viktoriia Gross & Achille Fraisse & Chetan Aditya & Mariela Furstenheim & Gregory Batt, 2022. "Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Sebastián Sosa-Carrillo & Henri Galez & Sara Napolitano & François Bertaux & Gregory Batt, 2023. "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Jean-Baptiste Lugagne & Caroline M. Blassick & Mary J. Dunlop, 2024. "Deep model predictive control of gene expression in thousands of single cells," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Sant Kumar & Hannes M. Beyer & Mingzhe Chen & Matias D. Zurbriggen & Mustafa Khammash, 2024. "Image-guided optogenetic spatiotemporal tissue patterning using μPatternScope," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Zachary R. Fox & Steven Fletcher & Achille Fraisse & Chetan Aditya & Sebastián Sosa-Carrillo & Julienne Petit & Sébastien Gilles & François Bertaux & Jakob Ruess & Gregory Batt, 2022. "Enabling reactive microscopy with MicroMator," Nature Communications, Nature, vol. 13(1), pages 1-8, 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:plo:pcbi00:1007673. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.