TY  - JOUR
A1  - Staffler, Benedikt
A1  - Berning, Manuel
A1  - Boergens, Kevin M.
A1  - Gour, Anjali
A1  - Smagt, Patrick van der
A1  - Helmstädter, Moritz
T1  - SynEM, automated synapse detection for connectomics
T2  - eLife
N2  - 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.
KW  - Tools and resources
KW  - Neuroscience
KW  - Connectomics
KW  - Electron microscopy
KW  - Machine learning
KW  - Cerebral cortex
KW  - Synapses
Y1  - 2017
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/45776
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-457768
SN  - 2050-084X
N1  - 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.
VL  - 6
IS  - e26414
SP  - 1
EP  - 25
PB  - eLife Sciences Publications
CY  - Cambridge
ER  -