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SynEM, automated synapse detection for connectomics

  1. Benedikt Staffler
  2. Manuel Berning
  3. Kevin M Boergens
  4. Anjali Gour
  5. Patrick van der Smagt
  6. Moritz Helmstaedter Is a corresponding author
  1. Max Planck Institute for Brain Research, Germany
  2. Volkswagen Group, Germany
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Cite as: eLife 2017;6:e26414 doi: 10.7554/eLife.26414

Abstract

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Article and author information

Author details

  1. Benedikt Staffler

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Manuel Berning

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0002-3679-8363
  3. Kevin M Boergens

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Anjali Gour

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Patrick van der Smagt

    Data Lab, Volkswagen Group, Munich, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Moritz Helmstaedter

    Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
    For correspondence
    mh@brain.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0001-7973-0767

Funding

Max-Planck Society (Open access funding)

  • Benedikt Staffler
  • Manuel Berning
  • Kevin M Boergens
  • Anjali Gour
  • Moritz Helmstaedter

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All animal experiments were performed in accordance with the guidelines for the Use of Laboratory Animals of the Max Planck Society and approved by the local authorities Regierungspräsidium Oberbayern, AZ 55.2-1-54-2532.3-103-12.

Reviewing Editor

  1. Jeremy Nathans, Reviewing Editor, Johns Hopkins University School of Medicine, United States

Publication history

  1. Received: February 28, 2017
  2. Accepted: July 12, 2017
  3. Accepted Manuscript published: July 14, 2017 (version 1)

Copyright

© 2017, Staffler et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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