SynEM, automated synapse detection for connectomics
- Max Planck Institute for Brain Research, Germany
- Volkswagen Group, Germany
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.
Data availability
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SBEM data from mouse S1 cortex for SynEM development and validationPublicly available at Max Planck Compute Center.
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Data from: Saturated Reconstruction of a Volume of Neocortex.doi:10.1016/j.cell.2015.06.054.
Article and author information
Author details
Moritz Helmstaedter
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.
Reviewing Editor
- Jeremy Nathans, Johns Hopkins University School of Medicine, United States
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.
Version history
- Received: February 28, 2017
- Accepted: July 12, 2017
- Accepted Manuscript published: July 14, 2017 (version 1)
- Version of Record published: October 26, 2017 (version 2)
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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SynEM, automated synapse detection for connectomics
eLife 6:e26414.
https://doi.org/10.7554/eLife.26414
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