PT  - JOURNAL ARTICLE
AU  - Benedikt Staffler
AU  - Manuel Berning
AU  - Kevin M. Boergens
AU  - Anjali Gour
AU  - Patrick van der Smagt
AU  - Moritz Helmstaedter
TI  - SynEM: Automated synapse detection for connectomics
AID  - 10.1101/099994
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 099994
4099  - http://biorxiv.org/content/early/2017/01/22/099994.short
4100  - http://biorxiv.org/content/early/2017/01/22/099994.full
AB  - 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, of which about 80-90% are excitatory 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 excitatory 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 98% precision and recall in binary excitatory 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.