3-D Quantification of Filopodia in Motile Cancer Cells

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107178" target="_blank" >RIV/00216224:14330/19:00107178 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TMI.2018.2873842" target="_blank" >https://doi.org/10.1109/TMI.2018.2873842</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TMI.2018.2873842" target="_blank" >10.1109/TMI.2018.2873842</a>

Jazyk výsledku

angličtina

Název v původním jazyce

3-D Quantification of Filopodia in Motile Cancer Cells

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

3-D Quantification of Filopodia in Motile Cancer Cells

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ16-03909Y" target="_blank" >GJ16-03909Y: Vývoj spolehlivých metod pro automatizovanou kvantitativní charakterizaci buněčné motility ve fluorescenční mikroskopii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Medical Imaging

ISSN

0278-0062

e-ISSN

1558-254X

Svazek periodika

38

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

862-872

Kód UT WoS článku

000460662400019

EID výsledku v databázi Scopus

2-s2.0-85054522263