Synapse classification and localization in Electron Micrographs

Jagadeesh, Vignesh; Anderson, James R.; Jones, Bryan W.; Marc, Robert E.; Fisher, Steven K.; Manjunath, B.S.

doi:10.1016/j.patrec.2013.06.001

Cited by 21 publications

(18 citation statements)

References 8 publications

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“…We next compared SynEM to previously published synapse detection methods ( Figure 3f , Mishchenko et al, 2010 ; Kreshuk et al, 2011 , 2014 ; Becker et al, 2012 ; Roncal et al, 2015 ; Dorkenwald et al, 2017 ). Other published methods were either already shown to be inferior to one of these approaches ( Perez et al, 2014 ; Márquez Neila et al, 2016 ) or developed for specific subtypes of synapses, only ( Jagadeesh et al, 2014 ; Plaza et al, 2014 ; Huang et al, 2016 ); these were therefore not included in the comparison. SynEM outperforms the state-of-the-art methods when applied to our SBEM data acquired at 3537 nm 3 voxel size ( Figure 3f , Figure 3—figure supplement 3 ).…”

Section: Resultsmentioning

confidence: 99%

SynEM, automated synapse detection for connectomics

Staffler

Berning

Boergens

et al. 2017

eLife

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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. 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.DOI: http://dx.doi.org/10.7554/eLife.26414.001

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Section: Resultsmentioning

confidence: 99%

SynEM, automated synapse detection for connectomics

Staffler

Berning

Boergens

et al. 2017

eLife

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“…Jagadeesh et al ( 2013 ) consider the problem of large-scale synapse detection in a large image volume. They first use a fast interest point detector based on image-thresholding to generate proposals for possible synapse locations.…”

Section: Introductionmentioning

confidence: 99%

“…The above approaches were evaluated on synapse detection in mammalian tissues, assuming a single postsynaptic site for each presynaptic site. Several approaches also make additional assumptions on the data, such as being able to reliably identify the synaptic cleft to extract spatially consistent features (Becker et al, 2013 ) or having feature descriptors hand-tuned for particular biological structures (Jagadeesh et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Fully-Automatic Synapse Prediction and Validation on a Large Data Set

Huang

Scheffer

Plaza

2018

Front. Neural Circuits

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Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of connections (synapses) between neurons. As manual extraction of this information is very time-consuming, there has been extensive research efforts to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively little research on automatic detection of the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite for the first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections. However, recent research has demonstrated that contact area alone is not a sufficient predictor of a synaptic connection. Moreover, as segmentation improved, we observed that synapse annotation consumes a more significant fraction of overall reconstruction time (upwards of 50% of total effort). This ratio will only get worse as segmentation improves, gating the overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect presynaptic neurons and their postsynaptic partners. In particular, presynaptic structures are detected using a U-Net convolutional neural network (CNN), and postsynaptic partners are detected using a multilayer perceptron (MLP) with features conditioned on the local segmentation. This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. When applied to the output of our method on EM data from Drosphila, these metrics demonstrate that a completely automatic prediction can be used to effectively characterize most of the connectivity correctly.

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“…Navlakha et al [ 12 ] presented an original experimental technique for selectively staining synapses, and then they utilized a semi-supervised method to train classifiers such as support vector machine (SVM), AdaBoost and random forest to identify synapses. Similarly, Jagadeesh et al [ 13 ] presented a new method for synapse detection and localization. This method first characterized synaptic junctions as ribbons, vesicles and clefts, and then it utilized maximally stable extremal region (MSER) to design a detector to locate synapses.…”

Section: Introductionmentioning

confidence: 99%

Effective automated pipeline for 3D reconstruction of synapses based on deep learning

Xiao

Deng

et al. 2018

BMC Bioinformatics

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BackgroundThe locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation.ResultsWe propose a fully automated method that relies on deep learning to realize the 3D reconstruction of synapses in electron microscopy (EM) images. The proposed method consists of three main parts: (1) training and employing the faster region convolutional neural networks (R-CNN) algorithm to detect synapses, (2) using the z-continuity of synapses to reduce false positives, and (3) combining the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. Experimental results were validated by manual tracking, and the effectiveness of our proposed method was demonstrated. The experimental results in anisotropic and isotropic EM volumes demonstrate the effectiveness of our algorithm, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results.ConclusionsOur fully automated approach contributes to the development of neuroscience, providing neurologists with a rapid approach for obtaining rich synaptic statistics.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2232-0) contains supplementary material, which is available to authorized users.

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Synapse classification and localization in Electron Micrographs

Cited by 21 publications

References 8 publications

SynEM, automated synapse detection for connectomics

SynEM, automated synapse detection for connectomics

Fully-Automatic Synapse Prediction and Validation on a Large Data Set

Effective automated pipeline for 3D reconstruction of synapses based on deep learning

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