Segmentation of Actin-Stained 3D Fluorescent Cells with Filopodial Protrusions using Convolutional Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100837" target="_blank" >RIV/00216224:14330/18:00100837 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ISBI.2018.8363605" target="_blank" >http://dx.doi.org/10.1109/ISBI.2018.8363605</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISBI.2018.8363605" target="_blank" >10.1109/ISBI.2018.8363605</a>

Result language

angličtina

Original language name

Segmentation of Actin-Stained 3D Fluorescent Cells with Filopodial Protrusions using Convolutional Neural Networks

Original language description

We present the architecture, training strategy and evaluation of a convolutional neural network (CNN) designed for the segmentation of actin-stained cells in 3D+t confocal microscopy image data. The segmentation performance of the CNN is evaluated using time-lapse sequences of lung adenocarcinoma cells with three genetically distinct variants of the tubulin adaptor protein, a key protein in the process of assembly of the cell cytoskeleton, displaying three different phenotypes in regards to the morphology of the cells and in particular, to the number and length of filopodial structures. We show that the CNN significantly outperforms a baseline method based on the minimization of the Chan-Vese model using graph cuts, and we discuss the inherent benefits of using the CNN over the baseline method.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GJ16-03909Y" target="_blank" >GJ16-03909Y: Development of Reliable Methods for Automated Quantitative Characterization of Cell Motility in Fluorescence Microscopy</a><br>

Continuities

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

Publication year

2018

Confidentiality

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

Article name in the collection

15th IEEE International Symposium on Biomedical Imaging

ISBN

9781538636367

ISSN

1945-7928

e-ISSN

—

Number of pages

5

Pages from-to

413-417

Publisher name

IEEE

Place of publication

Washington

Event location

Washington, USA

Event date

Jan 1, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000455045600094