3-D Quantification of Filopodia in Motile Cancer Cells

J 2019

3-D Quantification of Filopodia in Motile Cancer Cells

CASTILLA, Carlos, Martin MAŠKA, Dmitry SOROKIN, Erik MEIJERING, Carlos ORTIZ-DE-SOLÓRZANO et. al.

Základní údaje

Originální název

3-D Quantification of Filopodia in Motile Cancer Cells

Autoři

CASTILLA, Carlos (724 Španělsko), Martin MAŠKA (203 Česká republika, garant, domácí), Dmitry SOROKIN (643 Rusko, domácí), Erik MEIJERING (528 Nizozemské království) a Carlos ORTIZ-DE-SOLÓRZANO (724 Španělsko)

Vydání

IEEE Transactions on Medical Imaging, IEEE, 2019, 0278-0062

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: 6.685

Kód RIV

RIV/00216224:14330/19:00107178

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1109/TMI.2018.2873842

UT WoS

000460662400019

Klíčová slova anglicky

Filopodium segmentation and tracking;actin cytoskeleton;confocal microscopy;3D skeletonization;Chan-Vese model;convolutional neural network;deep learning

Štítky

cbia-web

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 12. 4. 2020 08:31, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.

Návaznosti

GJ16-03909Y, projekt VaV

Název: Vývoj spolehlivých metod pro automatizovanou kvantitativní charakterizaci buněčné motility ve fluorescenční mikroskopii

Investor: Grantová agentura ČR, Vývoj spolehlivých metod pro automatizovanou kvantitativní charakterizaci buněčné motility ve fluorescenční mikroskopii

Citovat

CASTILLA, Carlos, Martin MAŠKA, Dmitry SOROKIN, Erik MEIJERING a Carlos ORTIZ-DE-SOLÓRZANO. 3-D Quantification of Filopodia in Motile Cancer Cells. IEEE Transactions on Medical Imaging. IEEE, 2019, roč. 38, č. 3, s. 862-872. ISSN 0278-0062. Dostupné z: https://dx.doi.org/10.1109/TMI.2018.2873842.

@article{1442158,
   author = {Castilla, Carlos and Maška, Martin and Sorokin, Dmitry and Meijering, Erik and OrtizanddeandSolórzano, Carlos},
   article_number = {3},
   doi = {http://dx.doi.org/10.1109/TMI.2018.2873842},
   keywords = {Filopodium segmentation and tracking;actin cytoskeleton;confocal microscopy;3D skeletonization;Chan-Vese model;convolutional neural network;deep learning},
   language = {eng},
   issn = {0278-0062},
   journal = {IEEE Transactions on Medical Imaging},
   title = {3-D Quantification of Filopodia in Motile Cancer Cells},
   url = {https://doi.org/10.1109/TMI.2018.2873842},
   volume = {38},
   year = {2019}
}

TY  - JOUR
ID  - 1442158
AU  - Castilla, Carlos - Maška, Martin - Sorokin, Dmitry - Meijering, Erik - Ortiz-de-Solórzano, Carlos
PY  - 2019
TI  - 3-D Quantification of Filopodia in Motile Cancer Cells
JF  - IEEE Transactions on Medical Imaging
VL  - 38
IS  - 3
SP  - 862-872
EP  - 862-872
PB  - IEEE
SN  - 02780062
KW  - Filopodium segmentation and tracking;actin cytoskeleton;confocal microscopy;3D skeletonization;Chan-Vese model;convolutional neural network;deep learning
UR  - https://doi.org/10.1109/TMI.2018.2873842
L2  - https://doi.org/10.1109/TMI.2018.2873842
N2  - We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.
ER  -

CASTILLA, Carlos, Martin MAŠKA, Dmitry SOROKIN, Erik MEIJERING a Carlos ORTIZ-DE-SOLÓRZANO. 3-D Quantification of Filopodia in Motile Cancer Cells. \textit{IEEE Transactions on Medical Imaging}. IEEE, 2019, roč.~38, č.~3, s.~862-872. ISSN~0278-0062. Dostupné z: https://dx.doi.org/10.1109/TMI.2018.2873842.

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