SynEM: Automated synapse detection for connectomics

Staffler, Benedikt; Berning, Manuel; Boergens, Kevin M.; Gour, Anjali; Smagt, Patrick van der; Helmstaedter, Moritz

doi:10.1101/099994

Cited by 12 publications

(37 citation statements)

References 37 publications

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“…Another group of recent approaches do not explicitly represent synaptic clefts [69,72]. Buhmann et al [69] detect sites where presynaptic and postsynaptic terminals are separated by specific spatial intervals, using a single convolutional net to predict both location and directionality of synapses.…”

Section: Synaptic Relationshipsmentioning

confidence: 99%

“…Buhmann et al [69] detect sites where presynaptic and postsynaptic terminals are separated by specific spatial intervals, using a single convolutional net to predict both location and directionality of synapses. Staffler et al [72] extract contact sites for all adjacent pairs of segments within the region of interest, and use a decision tree trained on hand-designed features of the "subsegments" close to the contact site in order to infer synaptic contact sites and their directionality of connection.…”

Section: Synaptic Relationshipsmentioning

confidence: 99%

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Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Lee¹,

Turner

Macrina

et al. 2019

Current Opinion in Neurobiology

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Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes.

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Section: Synaptic Relationshipsmentioning

confidence: 99%

Section: Synaptic Relationshipsmentioning

confidence: 99%

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Lee¹,

Turner

Macrina

et al. 2019

Current Opinion in Neurobiology

View full text Add to dashboard Cite

show abstract

“…In terms of complexity, identification of neural connectivity is as challenging as tracing the neurons [13]. With rapid and outstanding improvement in automated EM segmentation in recent years, detection of synaptic connectivity may soon become a bottleneck in the overall neural reconstruction process [14]. Although fewer in number when compared against those in neurite segmentation, there are past studies on synaptic connectivity detection; we mention some notable works in the relevant literature section below.…”

Section: Introductionmentioning

confidence: 99%

“…Despite many discernible merits of previous works, very few of them aim to identify both the location and direction of synaptic junctions. Among these few methods, namely by [15][13] [14], none of them have been shown to be generally applicable on different types of synapses typically found on different species of animals, e.g., dyadic connections in vertebrate (mouse, rat, zebrafinch, etc.) and polyadic connections in non-vertebrate (fruit fly) brain 2 .…”

Section: Introductionmentioning

confidence: 99%

Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets

Parag

Berger

Kamentsky

et al. 2019

Lecture Notes in Computer Science

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Synaptic connectivity detection is a critical task for neural reconstruction from Electron Microscopy (EM) data. Most of the existing algorithms for synapse detection do not identify the cleft location and direction of connectivity simultaneously. The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types. In this paper, we present an algorithm to automatically predict the location as well as the direction of both dyadic and polyadic synapses. The proposed algorithm first generates candidate synaptic connections from voxelwise predictions of signed proximity generated by a 3D U-net. A second 3D CNN then prunes the set of candidates to produce the final detection of cleft and connectivity orientation. Experimental results demonstrate that the proposed method outperforms the existing methods for determining synapses in both rodent and fruit fly brain 1 .

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“…Advances in volume electron microscopy (VEM) have led to increasingly large 3D images of brain tissue, making manual analysis infeasible 1 . Multi-beam scanning electron microscopes 2 and transmission electron microscopes equipped with fast camera arrays can now generate data sets exceeding 100 TB 3 , a development which was fortunately accompanied by substantial progress in neuron reconstruction [4][5][6][7][8][9] and the automatic analysis of synapses [10][11][12][13] . These advances enable now automatic morphology analyses on the neuron (fragment) scale, which were previously restricted to direct segmentation error detection 5,14 , or used manual skeletons with data-specific hand-crafted features 11,15,16 .…”

Section: Introductionmentioning

confidence: 99%

Learning cellular morphology with neural networks

Schubert

Dorkenwald

Jain

et al. 2018

Preprint

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Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging, but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction, as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite their diverse application possibilities. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training we inferred morphology embeddings ("Neuron2vec") of neuron reconstructions and trained CMNs to identify glia cells in a supervised classification paradigm which was used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.

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SynEM: Automated synapse detection for connectomics

Abstract: ABSTRACT

Cited by 12 publications

References 37 publications

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets

Learning cellular morphology with neural networks

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