Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107638" target="_blank" >RIV/00216224:14330/19:00107638 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1038/s41598-019-49431-3" target="_blank" >http://dx.doi.org/10.1038/s41598-019-49431-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-019-49431-3" target="_blank" >10.1038/s41598-019-49431-3</a>

Result language
angličtina
Original language name
Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images
Original language description
Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, refecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantifcation of sEVs an extremely difcult task. We present a completely deep-learningbased pipeline for the segmentation of seVs in teM images. our method applies a residual convolutional neural network to obtain fne masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two diferent state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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