Automated synaptic connectivity inference for volume electron microscopy

Dorkenwald, Sven; Schubert, Philipp J; Killinger, Marius F; Urban, Gregor; Mikula, Shawn; Svara, Fabian; Kornfeld, Joergen

doi:10.1038/nmeth.4206

Cited by 131 publications

(152 citation statements)

References 43 publications

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“…It might take the better part of a year merely to acquire the raw data (Schalek et al, 2016). While even a few years ago it seemed impossible to analyze such an amount of data within a reasonable time frame, recent progress in the automation of segmentation are encouraging (Berning et al, 2015; Januszewski et al, 2016; Beier et al, 2017; Dorkenwald et al, 2017). …”

Section: Discussionmentioning

confidence: 99%

EM connectomics reveals axonal target variation in a sequence-generating network

Kornfeld

Benezra

Narayanan

et al. 2017

eLife

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108

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The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.DOI: http://dx.doi.org/10.7554/eLife.24364.001

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Section: Discussionmentioning

confidence: 99%

EM connectomics reveals axonal target variation in a sequence-generating network

Kornfeld

Benezra

Narayanan

et al. 2017

eLife

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108

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“…First, using manual or semi-automated segmentation software tools (e.g., 27 TrackEM2 13 and ilastik 17 ) or pipelines (Chklovskii et al 20 ), would be prohibitively time consuming. Providing annotated data, i.e., training data, for supervised learning-based segmentation methods, such as SegEM 18 and SyConn 28 , is also very time consuming, or requires several annotators. Second, our interest lies in segmentation of several white matter constituents, such as myelin, myelinated and unmyelinated axons, cell bodies, mitochondria, and vacuoles.…”

Section: Introductionmentioning

confidence: 99%

Automated 3D Axonal Morphometry of White Matter

Abdollahzadeh

Belevich

Jokitalo

et al. 2017

Preprint

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Axonal structure underlies white matter functionality and plays a major role in brain connectivity. The current literature on axonal structure is based on the analysis of two-dimensional (2D) cross-sections, which, as we demonstrate, is precarious.We developed a novel pipeline for automated three-dimensional (3D) segmentation and morphometric analysis (ACSON) of the white matter ultrastructure. The automated pipeline eliminates the need for time-consuming manual segmentation of 3D datasets. We applied the pipeline to serial block-face scanning electron microscopy (SBEM) images of the corpus callosum of sham-operated and traumatic brain injury rats (n=1/condition) 5 months after injury. The 3D morphometry showed a substantial variation in axonal diameter along individual axons and the axonal cross-sections were elliptic rather than circular. The axonal diameter was significantly greater in the traumatic brain injured rat, indicating ongoing axonal alterations even at this chronic time-point.

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“…Cireşan [58] et al applied deep neural networks (DNN) to detect membrane neuronal and mitosis detection in breast cancer [59]. Within EM studies, deep learning has been applied to analyse mitochondria [60,61], synapses [62] and proteins [63].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

Karabağ

Jones

Peddie

et al. 2020

Preprint

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In this work, images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and three deep learning architectures: VGG16, ResNet18 and Inception-ResNet-v2. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%).

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Automated synaptic connectivity inference for volume electron microscopy

Cited by 131 publications

References 43 publications

EM connectomics reveals axonal target variation in a sequence-generating network

EM connectomics reveals axonal target variation in a sequence-generating network

Automated 3D Axonal Morphometry of White Matter

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

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