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Journal Article

SynEM, automated synapse detection for connectomics

MPS-Authors

Staffler,  Benedikt
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

Berning,  Manuel
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

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Boergens,  Kevin M.
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

Gour,  Anjali
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

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Helmstaedter,  Moritz
Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society;

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Citation

Staffler, B., Berning, M., Boergens, K. M., Gour, A., van der Smagt, P., & Helmstaedter, M. (2017). SynEM, automated synapse detection for connectomics. eLIFE. doi:10.7554/eLife.26414.


Cite as: http://hdl.handle.net/21.11116/0000-0000-F56F-0
Abstract
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 tissues, dense connetomic 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 ysnaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortcial neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped conncectomes.