IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0125825.html
   My bibliography  Save this article

A Context-Aware Delayed Agglomeration Framework for Electron Microscopy Segmentation

Author

Listed:
  • Toufiq Parag
  • Anirban Chakraborty
  • Stephen Plaza
  • Louis Scheffer

Abstract

Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a “delayed” scheme for agglomerative clustering that postpones some of the merge decisions, pertaining to newly formed bodies, in order to generate a more confident boundary prediction. We report significant improvements attained by the proposed approach in segmentation accuracy over existing standard methods on 2D and 3D datasets.

Suggested Citation

  • Toufiq Parag & Anirban Chakraborty & Stephen Plaza & Louis Scheffer, 2015. "A Context-Aware Delayed Agglomeration Framework for Electron Microscopy Segmentation," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-19, May.
    • Handle: RePEc:plo:pone00:0125825
    DOI: 10.1371/journal.pone.0125825
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125825
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0125825&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0125825?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. Shin-ya Takemura & Arjun Bharioke & Zhiyuan Lu & Aljoscha Nern & Shiv Vitaladevuni & Patricia K. Rivlin & William T. Katz & Donald J. Olbris & Stephen M. Plaza & Philip Winston & Ting Zhao & Jane Anne, 2013. "A visual motion detection circuit suggested by Drosophila connectomics," Nature, Nature, vol. 500(7461), pages 175-181, August.
    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. Daniel Soudry & Suraj Keshri & Patrick Stinson & Min-hwan Oh & Garud Iyengar & Liam Paninski, 2015. "Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-30, October.
    2. Shang, Ke-ke & Small, Michael & Yan, Wei-sheng, 2017. "Link direction for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 767-776.
    3. Antoine Allard & M Ángeles Serrano, 2020. "Navigable maps of structural brain networks across species," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-20, February.
    4. Jacqueline Cornean & Sebastian Molina-Obando & Burak Gür & Annika Bast & Giordano Ramos-Traslosheros & Jonas Chojetzki & Lena Lörsch & Maria Ioannidou & Rachita Taneja & Christopher Schnaitmann & Mari, 2024. "Heterogeneity of synaptic connectivity in the fly visual system," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Kit D. Longden & Edward M. Rogers & Aljoscha Nern & Heather Dionne & Michael B. Reiser, 2023. "Different spectral sensitivities of ON- and OFF-motion pathways enhance the detection of approaching color objects in Drosophila," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    6. Saha, Papri & Sarkar, Debasish, 2022. "Allometric scaling of von Neumann entropy in animal connectomes and its evolutionary aspect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).

    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:pone00:0125825. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.