ScanEV - A neural network-based tool for the automated detection of extracellular vesicles in TEM images

Igor Nikishin; Ruslan Dulimov; Gleb Skryabin; Sergey Galetsky; Elena Tchevkina; Dmitry Bagrov

doi:10.1016/j.micron.2021.103044

ScanEV - A neural network-based tool for the automated detection of extracellular vesicles in TEM images

Micron. 2021 Jun:145:103044. doi: 10.1016/j.micron.2021.103044. Epub 2021 Feb 26.

Authors

Igor Nikishin¹, Ruslan Dulimov², Gleb Skryabin³, Sergey Galetsky³, Elena Tchevkina³, Dmitry Bagrov⁴

Affiliations

¹ Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia.
² VISME LLC, Moscow, Russia.
³ N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russia.
⁴ Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia. Electronic address: bagrov@mail.bio.msu.ru.

PMID: 33676158
DOI: 10.1016/j.micron.2021.103044

Abstract

Transmission electron microscopy (TEM) is the most widely accepted method for visualization of extracellular vesicles (EVs), and particularly, exosomes. TEM images provide us with information about the size and morphology of the EVs. We have developed an online tool ScanEV (Scanner for the Extracellular Vesicles, available at https://bioeng.ru/scanev), for the rapid and automated processing of such images. ScanEV is based on a convolutional neural network; it detects the «cup-shaped» particles in the images and calculates their morphometric parameters. This tool will be useful for researchers who study EVs and use TEM for their characterization.

Keywords: Extracellular vesicles; Image processing; Neural network; Transmission electron microscopy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Exosomes*
Extracellular Vesicles*
Microscopy, Electron, Transmission
Neural Networks, Computer