TY - JOUR T1 - SynEM: Automated synapse detection for connectomics JF - bioRxiv DO - 10.1101/099994 SP - 099994 AU - Benedikt Staffler AU - Manuel Berning AU - Kevin M. Boergens AU - Anjali Gour AU - Patrick van der Smagt AU - Moritz Helmstaedter Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/22/099994.abstract 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, 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. ER -