Procedings of the British Machine Vision Conference 2015 2015
DOI: 10.5244/c.29.81
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VESICLE: Volumetric Evaluation of Synaptic Inferfaces using Computer Vision at Large Scale

Abstract: An open challenge at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced understanding of the wiring diagram of the brain promises to lead to new breakthroughs in diagnosing and treating neurological disorders. Inferring brain structure from image data, such as that obtained via electron microscopy (EM), entails solving the problem of identifying biological structures in large data volumes. Synapses, which are a key co… Show more

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Cited by 26 publications
(51 citation statements)
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“…Figure 3(a) compares the precision recall curves for detecting both location and connectivity with two variants. 1) 3 Label + pruning -where the proximity approximation is replaced by 3-class classification among pre-, post-synaptic, and rest of the voxels (Figure 1 This experiment suggests that the pruning network is substantially more effective than morphological post-processing [23]. The proposed signed proximity approximation yields 3% more true positives in the initial candidate set than those generated by the multiclass prediction.…”
Section: Rat Cortexmentioning
confidence: 97%
See 1 more Smart Citation
“…Figure 3(a) compares the precision recall curves for detecting both location and connectivity with two variants. 1) 3 Label + pruning -where the proximity approximation is replaced by 3-class classification among pre-, post-synaptic, and rest of the voxels (Figure 1 This experiment suggests that the pruning network is substantially more effective than morphological post-processing [23]. The proposed signed proximity approximation yields 3% more true positives in the initial candidate set than those generated by the multiclass prediction.…”
Section: Rat Cortexmentioning
confidence: 97%
“…These algorithms assumed subsequent human intervention to determine the synaptic partners given the cleft predictions. Roncal et al [23] combine the information provided by membrane and vesicle predictions with a CNN (not fully convolutional) and apply post-processing to locate synaptic clefts. To establish the pre-and post-synaptic partnership, [24] augmented the synaptic cleft detection with a multilayer perceptron operating on hand designed features.…”
Section: Relevant Literaturementioning
confidence: 99%
“…Examples of tools for membrane segmentation include CNN [30] and U-nets [29] approaches. Synapse detection has been achieved using deep learning techniques and random forest classifiers [31,32]. Neural segmentation has been previously done using agglomeration-based approaches [33] and automated selection of neural networks [9].…”
Section: Deriving Synapse-level Connectomes From Electron Microscopymentioning
confidence: 99%
“…Neural networks can take weeks or months to train, and feature extraction, while faster than training, is still inefficient even on a graphical processing unit (GPU). For just 100 images with a resolution of 1024 × 1024, a state-of-the-art network for connectomics takes 59 hours for training and evaluation 29 .…”
Section: Introductionmentioning
confidence: 99%
“…A wide variety of methods -highly dependent on supervised machine learning -exist for the dense EM segmentation and reconstruction problem. The methods include SVM-based algorithms [12][13][14][15][16] , Random Forests 12, 17-24 , Conditional Random Fields 22 , and Artificial Neural Networks [25][26][27][28][29][30] (i.e., deep learning). These machine learning approaches can be found in popular software packages for connectomics image analysis such as Rhoana 31 and Ilastik 18,19,32 .…”
Section: Introductionmentioning
confidence: 99%