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@article{1547981,
author = {Vičar, Tomáš and Balvan, Jan and Jaroš, Josef and Jug, Florian and Kolar, Radim and Masařík, Michal and Gumulec, Jaromír},
article_location = {London},
article_number = {360},
doi = {http://dx.doi.org/10.1186/s12859-019-2880-8},
keywords = {Microscopy; Cell segmentation; Image reconstruction; Methods comparison; Differential contrast image; Quantitative phase imaging; Laplacian of Gaussians},
language = {eng},
issn = {1471-2105},
journal = {BMC Bioinformatics},
title = {Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison},
url = {http://dx.doi.org/10.1186/s12859-019-2880-8},
volume = {20},
year = {2019}
}
TY - JOUR
ID - 1547981
AU - Vičar, Tomáš - Balvan, Jan - Jaroš, Josef - Jug, Florian - Kolar, Radim - Masařík, Michal - Gumulec, Jaromír
PY - 2019
TI - Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison
JF - BMC Bioinformatics
VL - 20
IS - 360
SP - 1-25
EP - 1-25
PB - BioMed Central
SN - 14712105
KW - Microscopy
KW - Cell segmentation
KW - Image reconstruction
KW - Methods comparison
KW - Differential contrast image
KW - Quantitative phase imaging
KW - Laplacian of Gaussians
UR - http://dx.doi.org/10.1186/s12859-019-2880-8
L2 - http://dx.doi.org/10.1186/s12859-019-2880-8
N2 - 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.
ER -
VIČAR, Tomáš, Jan BALVAN, Josef JAROŠ, Florian JUG, Radim KOLAR, Michal MASAŘÍK and Jaromír GUMULEC. Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison. \textit{BMC Bioinformatics}. London: BioMed Central, 2019, vol.~20, No~360, p.~1-25. ISSN~1471-2105. Available from: https://dx.doi.org/10.1186/s12859-019-2880-8.
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