SynEM, automated synapse detection for connectomics
- 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.
Author: | Benedikt Staffler, Manuel Berning, Kevin M. Boergens, Anjali Gour, Patrick van der Smagt, Moritz HelmstädterORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-457768 |
DOI: | https://doi.org/10.7554/eLife.26414 |
ISSN: | 2050-084X |
Pubmed Id: | https://pubmed.ncbi.nlm.nih.gov/28708060 |
Parent Title (English): | eLife |
Publisher: | eLife Sciences Publications |
Place of publication: | Cambridge |
Contributor(s): | Jeremy Nathans |
Document Type: | Article |
Language: | English |
Year of Completion: | 2017 |
Date of first Publication: | 2017/07/14 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2018/03/01 |
Tag: | Cerebral cortex; Connectomics; Electron microscopy; Machine learning; Neuroscience; Synapses; Tools and resources |
Volume: | 6 |
Issue: | e26414 |
Page Number: | 25 |
First Page: | 1 |
Last Page: | 25 |
Note: | Copyright Staffler et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. |
HeBIS-PPN: | 431527601 |
Institutes: | Biochemie, Chemie und Pharmazie / Pharmazie |
Angeschlossene und kooperierende Institutionen / MPI für Hirnforschung | |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - Namensnennung 4.0 |