Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU137750" target="_blank" >RIV/00216305:26620/20:PU137750 - isvavai.cz</a>

Result on the web

<a href="https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-25/issue-08/086502/Automated-interpretation-of-time-lapse-quantitative-phase-image-by-machine/10.1117/1.JBO.25.8.086502.full?SSO=1" target="_blank" >https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-25/issue-08/086502/Automated-interpretation-of-time-lapse-quantitative-phase-image-by-machine/10.1117/1.JBO.25.8.086502.full?SSO=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/1.JBO.25.8.086502" target="_blank" >10.1117/1.JBO.25.8.086502</a>

Result language

angličtina

Original language name

Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

Original language description

Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10610 - Biophysics

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2020

Confidentiality

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

Name of the periodical

JOURNAL OF BIOMEDICAL OPTICS

ISSN

1083-3668

e-ISSN

1560-2281

Volume of the periodical

25

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1-18

UT code for WoS article

000590144000010

EID of the result in the Scopus database

2-s2.0-85089630916