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

J 2019

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

GÓMEZ-DE-MARISCAL, Estibaliz; Martin MAŠKA; Anna KOTRBOVÁ; Vendula POSPÍCHALOVÁ; Pavel MATULA et al.

Základní údaje

Originální název

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

Autoři

GÓMEZ-DE-MARISCAL, Estibaliz; Martin MAŠKA ; Anna KOTRBOVÁ; Vendula POSPÍCHALOVÁ; Pavel MATULA a Arrate MUÑOZ-BARRUTIA

Vydání

Scientific Reports, 2019, 2045-2322

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.998

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/19:00107638

Organizační jednotka

Fakulta informatiky

DOI

https://doi.org/10.1038/s41598-019-49431-3

UT WoS

000485680900008

EID Scopus

2-s2.0-85072191409

Klíčová slova anglicky

image segmentation;deep learning;smal extracellular vesicles;transmission electron microscopy

Štítky

14314010, cbia-web

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 6. 2022 12:09, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

GA17-05048S, projekt VaV

Název: Segmentace a trekování živých buněk v multimodálních obrazech

Investor: Grantová agentura ČR, Segmentace a trekování živých buněk v multimodálních obrazech

GJ17-11776Y, projekt VaV

Název: Funkční a biochemická analýza extracelulárních váčků z ascitů pacientek s karcinomem vaječníku

Investor: Grantová agentura ČR, Funkční a biochemická analýza extracelulárních váčků z ascitů pacientek s karcinomem vaječníku

Citovat

GÓMEZ-DE-MARISCAL, Estibaliz; Martin MAŠKA; Anna KOTRBOVÁ; Vendula POSPÍCHALOVÁ; Pavel MATULA a Arrate MUÑOZ-BARRUTIA. Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images. Scientific Reports. 2019, roč. 9, č. 13211, s. 1-10. ISSN 2045-2322. Dostupné z: https://doi.org/10.1038/s41598-019-49431-3.

@article{1557819,
   author = {GómezanddeandMariscal, Estibaliz and Maška, Martin and Kotrbová, Anna and Pospíchalová, Vendula and Matula, Pavel and MuñozandBarrutia, Arrate},
   article_number = {13211},
   doi = {https://doi.org/10.1038/s41598-019-49431-3},
   keywords = {image segmentation;deep learning;smal extracellular vesicles;transmission electron microscopy},
   language = {eng},
   issn = {2045-2322},
   journal = {Scientific Reports},
   title = {Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images},
   url = {http://dx.doi.org/10.1038/s41598-019-49431-3},
   volume = {9},
   year = {2019}
}

TY  - JOUR
ID  - 1557819
AU  - Gómez-de-Mariscal, Estibaliz - Maška, Martin - Kotrbová, Anna - Pospíchalová, Vendula - Matula, Pavel - Muñoz-Barrutia, Arrate
PY  - 2019
TI  - Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images
JF  - Scientific Reports
VL  - 9
IS  - 13211
SP  - 1-10
EP  - 1-10
SN  - 20452322
KW  - image segmentation;deep learning;smal extracellular vesicles;transmission electron microscopy
UR  - http://dx.doi.org/10.1038/s41598-019-49431-3
L2  - http://dx.doi.org/10.1038/s41598-019-49431-3
N2  - 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.
ER  -

GÓMEZ-DE-MARISCAL, Estibaliz; Martin MAŠKA; Anna KOTRBOVÁ; Vendula POSPÍCHALOVÁ; Pavel MATULA a Arrate MUÑOZ-BARRUTIA. Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images. \textit{Scientific Reports}. 2019, roč.~9, č.~13211, s.~1-10. ISSN~2045-2322. Dostupné z: https://doi.org/10.1038/s41598-019-49431-3.

Přehled o publikaci