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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

<a href="/cs/project/GA18-24089S" target="_blank" >GA18-24089S: Kvantitativní fázová mikroskopie pro 3D kvalitativní charakterizaci nádorových buněk</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

BMC Bioinformatics

ISSN

1471-2105

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

N/A

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

25

Strana od-do

—

Kód UT WoS článku

000473132400006

EID výsledku v databázi Scopus

—