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| Item Type: | Article |
|---|---|
| Title: | Automated detection and localization of synaptic vesicles in electron microscopy images |
| Creators Name: | Imbrosci, B. and Schmitz, D. and Orlando, M. |
| Abstract: | Information transfer and integration in the brain occurs at chemical synapses and is mediated by the fusion of synaptic vesicles filled with neurotransmitter. Synaptic vesicle dynamic spatial organization regulates synaptic transmission as well as synaptic plasticity. Because of their small size, synaptic vesicles require electron microscopy for their imaging, and their analysis is conducted manually. The manual annotation and segmentation of the hundreds to thousands of synaptic vesicles, is highly time consuming and limits the throughput of data collection. To overcome this limitation, we built an algorithm, mainly relying on convolutional neural networks, capable of automatically detecting and localizing synaptic vesicles in electron micrographs. The algorithm was trained on murine synapses but we show that it works well on synapses from different species, ranging from zebrafish to human, and from different preparations. As output, we provide the vesicles count and coordinates, the nearest neighbor distance and the estimate of the vesicles area. We also provide a graphical user interface (GUI) to guide users through image analysis, result visualization and manual proof-reading. The application of our algorithm is especially recommended for images produced by transmission electron microscopy. Since this type of imaging is used routinely to investigate presynaptic terminals, our solution will likely be of interest for numerous research groups. SIGNIFICANCE STATEMENT: The analysis of synaptic vesicles provides important insights towards the understanding of synaptic transmission and plasticity mechanisms. However, up to date, this analysis is still a very time-consuming manual process. In the present study we present a user-friendly algorithm, mainly based on convolutional neural networks, for automating the detection of synaptic vesicles in electron micrographs. This approach allows faster and more standardized analyses. |
| Keywords: | Automated Detection, Convolutional Neural Networks, Image Analysis, Machine Learning, Synaptic Vesicle, Animals, Mice, Zebrafish |
| Source: | eNeuro |
| ISSN: | 2373-2822 |
| Publisher: | Society for Neuroscience |
| Volume: | 9 |
| Number: | 1 |
| Page Range: | ENEURO.0400-20.2021 |
| Date: | January 2022 |
| Additional Information: | Erratum in: eNeuro 9(2):ENEURO.0123-22.2022 |
| Official Publication: | https://doi.org/10.1523/ENEURO.0400-20.2021 |
| PubMed: | View item in PubMed |
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