This dataset consist of transmission electron microscopy images used to train, test and evaluate the performance of an algorithm for the automated detection of synaptic vesicles. The code is available at https://github.com/Imbrosci/synaptic-vesicles-detection

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README.md

synaptic-vesicles-detection

In this repository you can find transmission electron microscopy (EM) images used to train and test an algorithm for the automated detection of synaptic vesicles. The algorithm was developed, and therefore works at best, on transmission EM images. The algorithm is available in the following GitHub repository: https://github.com/Imbrosci/synaptic-vesicles-detection. The images in this repository were used in a scientific publication, currently under revision, describing the algorithm and analysing extensively its performance (Imbrosci et al., under revision at eNeuro). Briefly, the algorithm uses two consecutive classifiers based on convolutional neural networks and afterward, a threshold-based segmentation, a connected-component labelling and k-means clustering algorithm. The first 50 images were used to generate small patches, either containing (pos) or not containing (neg) a vesicle in the middle. These patches were used to train and test the two classifiers. The patches to train and test the first classifier and the second (also called refinement classifier) can be found in the patches.zip file. Concerning the patches generation, it is worth noting that in some images, a black mask was used to cover the regions outside the synaptic terminal. The reason for using this mask was that the labelling of the synaptic vesicles was often limited to the synaptic terminal while vesicles were present in the images outside this structures as well. For this reason, some patches, originated from the border of a synaptic terminal, may have a black portion. The tiny white border visible in some patches is likely originated during the saving procedure. The remaining 37 images were used to test the performance of the algorithm in its entirity. In the metadata file, it is possible to find additional information regarding the images and the patches.

datacite.yml
Title Automated detection and localization of synaptic vesicles in electron microscopy images
Authors Imbrosci,Barbara;German Center for Neurodegenerative Diseases (DZNE) and NeuroCure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany;ORCID:ORCID:0000-0002-4527-0536
Schmitz,Dietmar;Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of Health, and NeuroCure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany; 1German Center for Neurodegenerative Diseases (DZNE) Berlin, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience (BCCN) Berlin, 10115 Berlin, Germany; Einstein Center for Neurosciences (ECN) Berlin, 10117 Berlin, Germany; Max-Delbrück-Centrum (MDC) for molecular medicine, 13125 Berlin, Germany;ORCID:0000-0003-2741-5241
Orlando,Marta;Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of Health, and NeuroCure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany;ORCID:0000-0002-9017-0251
Description This repository conitains 87 electron microscopy images of hippocampal murine presynaptic terminals that have been used to train, test and evaluate the performance of an algorithm for the automated detection and analysis of synaptic vesicles.
License Creative Commons CC0 4.0 attribution licence (https://creativecommons.org/licenses/by/4.0/)
References Imbrosci B, Schmitz D, Orlando M. Automated detection and localization of synaptic vesicles in electron microscopy images. To date, October 2021, is under revision at eNeuro [na] (IsSupplementTo)
Funding DFG, DFG.Exc2049
Keywords Neuroscience
Synapses
Synaptic vesicles
convolutional neural network
Resource Type Dataset