Quasi-spectral characterization of intracellular regions in bright-field light microscopy images
Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bri...
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| Published in | Scientific reports Vol. 10; no. 1; p. 18346 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
27.10.2020
Nature Publishing Group |
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| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-020-75441-7 |
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| Abstract | Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bright-field images in a model-free way. Here we introduce a method of a physically correct extraction of features for each pixel when these features resemble a transparency spectrum. The method is compatible with existent ordinary bright-field microscopes and requires mathematically sophisticated data processing. Unsupervised clustering of the spectra yields reasonable semantic segmentation of unstained living cells without any a priori information about their structures. Despite the lack of reference data (to prove strictly that the proposed feature vectors coincide with transparency), we believe that this method is the right approach to an intracellular (semi)quantitative and qualitative chemical analysis. |
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| AbstractList | Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bright-field images in a model-free way. Here we introduce a method of a physically correct extraction of features for each pixel when these features resemble a transparency spectrum. The method is compatible with existent ordinary bright-field microscopes and requires mathematically sophisticated data processing. Unsupervised clustering of the spectra yields reasonable semantic segmentation of unstained living cells without any a priori information about their structures. Despite the lack of reference data (to prove strictly that the proposed feature vectors coincide with transparency), we believe that this method is the right approach to an intracellular (semi)quantitative and qualitative chemical analysis. Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bright-field images in a model-free way. Here we introduce a method of a physically correct extraction of features for each pixel when these features resemble a transparency spectrum. The method is compatible with existent ordinary bright-field microscopes and requires mathematically sophisticated data processing. Unsupervised clustering of the spectra yields reasonable semantic segmentation of unstained living cells without any a priori information about their structures. Despite the lack of reference data (to prove strictly that the proposed feature vectors coincide with transparency), we believe that this method is the right approach to an intracellular (semi)quantitative and qualitative chemical analysis.Investigation of cell structure is hardly imaginable without bright-field microscopy. Numerous modifications such as depth-wise scanning or videoenhancement make this method being state-of-the-art. This raises a question what maximal information can be extracted from ordinary (but well acquired) bright-field images in a model-free way. Here we introduce a method of a physically correct extraction of features for each pixel when these features resemble a transparency spectrum. The method is compatible with existent ordinary bright-field microscopes and requires mathematically sophisticated data processing. Unsupervised clustering of the spectra yields reasonable semantic segmentation of unstained living cells without any a priori information about their structures. Despite the lack of reference data (to prove strictly that the proposed feature vectors coincide with transparency), we believe that this method is the right approach to an intracellular (semi)quantitative and qualitative chemical analysis. |
| ArticleNumber | 18346 |
| Author | Lonhus, Kirill Rychtáriková, Renata Platonova, Ganna Štys, Dalibor |
| Author_xml | – sequence: 1 givenname: Kirill surname: Lonhus fullname: Lonhus, Kirill email: lonhus@frov.jcu.cz organization: University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Kompetenzzentrum MechanoBiologie in Regenerativer Medizin, Institute of Complex Systems – sequence: 2 givenname: Renata surname: Rychtáriková fullname: Rychtáriková, Renata organization: University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Kompetenzzentrum MechanoBiologie in Regenerativer Medizin, Institute of Complex Systems – sequence: 3 givenname: Ganna surname: Platonova fullname: Platonova, Ganna organization: University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Kompetenzzentrum MechanoBiologie in Regenerativer Medizin, Institute of Complex Systems – sequence: 4 givenname: Dalibor surname: Štys fullname: Štys, Dalibor organization: University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Kompetenzzentrum MechanoBiologie in Regenerativer Medizin, Institute of Complex Systems |
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| References_xml | – reference: WangYYangBFengSPessinoVHuangBMulticolor fluorescent imaging by space-constrained computational spectral imagingOpt. Express20192753932019OExpr..27.5393W1:CAS:528:DC%2BC1MXhs1KltLrM10.1364/OE.27.005393 – reference: PearsonKOn lines and planes of closest fit to systems of points in spaceLond. Edinb. Dublin Philos. Mag. J. Sci.1901255957210.1080/14786440109462720 – reference: Pech-Pacheco, J., Cristobal, G., Chamorro-Martinez, J. & Fernandez-Valdivia, J. Diatom autofocusing in brightfield microscopy: a comparative study. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 (IEEE Comput. Soc., 2002). – reference: LichtscheidlIKFoissnerIVideo microscopy of dynamic plant cell organelles: principles of the technique and practical applicationJ. Microsc.199818111712810.1046/j.1365-2818.1996.105385.x – reference: HeistS5D hyperspectral imaging: fast and accurate measurement of surface shape and spectral characteristics using structured lightOpt. Express201826233662018OExpr..2623366H10.1364/OE.26.023366 – reference: TibshiraniRWaltherGHastieTEstimating the number of clusters in a data set via the gap statisticJ. R. Stat. Soc. B200163411423184150310.1111/1467-9868.00293 – reference: Velleman, D. J. The generalized Simpson’s rule. Am. Math. Monthly112, 342 (2005). – reference: StephensonWTechnique of factor analysisNature19351362971935Natur.136..297S10.1038/136297b0 – reference: CannyJA computational approach to edge detectionIEEE Trans. Pattern Anal. Mach. Intell.1986PAMI–867969810.1109/TPAMI.1986.4767851 – reference: Lonhus, K., Rychtáriková, R., Platonova, G. & Štys, D. Supplementary to Quasi-spectral characterization of intracellular regions in bright-field light microscopy images. Dryad Dataset.https://doi.org/10.5061/dryad.w0vt4b8p6. – reference: DahlbergPDA simple approach to spectrally resolved fluorescence and bright field microscopy over select regions of interestRev. Sci. Instrum.2016871137042016RScI...87k3704D10.1063/1.4967274 – reference: GariniYYoungITMcNamaraGSpectral imaging: principles and applicationsCytometry A200669A73574710.1002/cyto.a.20311 – reference: LindnerMShotanZGariniYRapid microscopy measurement of very large spectral imagesOpt. Express20162495112016OExpr..24.9511L10.1364/OE.24.009511 – reference: Rychtáriková, R. et al. Super-resolved 3-d imaging of live cells organelles’ from bright-field photon transmission micrographs. Ultramicroscopy179, 1–14 (2017). – reference: WachmanESSimultaneous imaging of cellular morphology and multiple biomarkers using an acousto-optic tunable filter–based bright field microscopeJ. Biomed. 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| Title | Quasi-spectral characterization of intracellular regions in bright-field light microscopy images |
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