SynEM, automated synapse detection for connectomics

Bibliographic Details
Title:	SynEM, automated synapse detection for connectomics
Authors:	Benedikt Staffler, Manuel Berning, Kevin M Boergens, Anjali Gour, Patrick van der Smagt, Moritz Helmstaedter
Source:	eLife, Vol 6 (2017)
Publisher Information:	eLife Sciences Publications Ltd, 2017.
Publication Year:	2017
Collection:	LCC:Medicine LCC:Science LCC:Biology (General)
Subject Terms:	connectomics, electron microscopy, machine learning, cerebral cortex, synapses, Medicine, Science, Biology (General), QH301-705.5
More Details:	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.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2050-084X
Relation:	https://elifesciences.org/articles/26414; https://doaj.org/toc/2050-084X
DOI:	10.7554/eLife.26414
Access URL:	https://doaj.org/article/c285b8bcd65d46fcb95d896506d553e9
Accession Number:	edsdoj.285b8bcd65d46fcb95d896506d553e9
Database:	Directory of Open Access Journals

More Details
ISSN:	2050084X
DOI:	10.7554/eLife.26414
Published in:	eLife
Language:	English