Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F19%3A00071027" target="_blank" >RIV/00159816:_____/19:00071027 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/19:00107532 RIV/00216305:26220/19:PU133046

Result on the web

<a href="https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-2880-8" target="_blank" >https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-2880-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12859-019-2880-8" target="_blank" >10.1186/s12859-019-2880-8</a>

Result language

angličtina

Original language name

Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

Original language description

BackgroundBecause of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities.ResultsWe built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online.ConclusionsWe demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

10608 - Biochemistry and molecular biology

Project

<a href="/en/project/GA18-24089S" target="_blank" >GA18-24089S: Quantitative phase microscopy for 3D qualitative characterization of cancer cells</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

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

Name of the periodical

BMC Bioinformatics

ISSN

1471-2105

e-ISSN

—

Volume of the periodical

20

Issue of the periodical within the volume

N/A

Country of publishing house

GB - UNITED KINGDOM

Number of pages

25

Pages from-to

—

UT code for WoS article

000473132400006

EID of the result in the Scopus database

—