دورية أكاديمية
Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison
| العنوان: | Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison |
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| المؤلفون: | Tomas Vicar, Jan Balvan, Josef Jaros, Florian Jug, Radim Kolar, Michal Masarik, Jaromir Gumulec |
| المصدر: | BMC Bioinformatics, Vol 20, Iss 1, Pp 1-25 (2019) |
| بيانات النشر: | BMC, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| مصطلحات موضوعية: | Microscopy, Cell segmentation, Image reconstruction, Methods comparison, Differential contrast image, Quantitative phase imaging, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| الوصف: | Abstract 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2105 |
| Relation: | http://link.springer.com/article/10.1186/s12859-019-2880-8; https://doaj.org/toc/1471-2105 |
| DOI: | 10.1186/s12859-019-2880-8 |
| URL الوصول: | https://doaj.org/article/bce2c3002c4a47e2a3008136bc467e8a |
| رقم الأكسشن: | edsdoj.bce2c3002c4a47e2a3008136bc467e8a |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14712105 |
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| DOI: | 10.1186/s12859-019-2880-8 |