Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison
Language English Country England, Great Britain Media electronic
Document type Journal Article, Review
Grant support
GACR 18-24089S
Grantová Agentura České Republiky
MUNI/A/1298/2017
Masaryk University
funds to Junior Researcher (Jaromir Gumulec)
Masaryk University Faculty of Medicine
PubMed
31253078
PubMed Central
PMC6599268
DOI
10.1186/s12859-019-2880-8
PII: 10.1186/s12859-019-2880-8
Knihovny.cz E-resources
- Keywords
- Cell segmentation, Differential contrast image, Image reconstruction, Laplacian of Gaussians, Methods comparison, Microscopy, Quantitative phase imaging,
- MeSH
- Algorithms MeSH
- Cell Fractionation methods MeSH
- Humans MeSH
- Microscopy methods MeSH
- Image Processing, Computer-Assisted MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
BACKGROUND: Because 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. RESULTS: We 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. CONCLUSIONS: We 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.
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