SynEM, automated synapse detection for connectomics

ELife
Benedikt StafflerMoritz Helmstaedter

Abstract

Nerve tissue contains a high density of chemical synapses, about 1 per µm(3) 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.

References

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Jun 13, 2017·Nature Methods·Kevin M BoergensMoritz Helmstaedter

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Citations

Sep 30, 2017·Nature·Helene SchmidtMoritz Helmstaedter
Oct 28, 2019·Science·Alessandro MottaMoritz Helmstaedter
May 28, 2019·Frontiers in Neuroinformatics·Xue Fan, Henry Markram
Nov 5, 2019·Frontiers in Neural Circuits·Yoshiyuki Kubota
Nov 14, 2018·Frontiers in Neural Circuits·Gary B HuangStephen M Plaza
Nov 10, 2018·Frontiers in Neural Circuits·Adrian A Wanner, Ashwin Vishwanathan
Dec 5, 2020·Science·Anjali GourMoritz Helmstaedter
May 15, 2021·Nature Communications·Emmanuel KlingerMoritz Helmstaedter

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Software Mentioned

Ilastik
SyConn
FIB
webKnossos
Tensor
SEM
ElektroNN
SynEM
VesicleCNN
LogitBoost

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