One-shot phase-recovery using a cellphone RGB camera on a Jamin-Lebedeff microscope
Autoři:
Benedict Diederich aff001; Barbora Marsikova aff001; Brad Amos aff003; Rainer Heintzmann aff001
Působiště autorů:
Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
aff001; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Helmholtzweg 4, 07745 Jena, Germany
aff002; Medical Research Council, MRC, Laboratory of Molecular Biology, Cambridge, United Kingdom
aff003
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227096
Souhrn
Jamin-Lebedeff (JL) polarization interference microscopy is a classical method for determining the change in the optical path of transparent tissues. Whilst a differential interference contrast (DIC) microscopy interferes an image with itself shifted by half a point spread function, the shear between the object and reference image in a JL-microscope is about half the field of view. The optical path difference (OPD) between the sample and reference region (assumed to be empty) is encoded into a color by white-light interference. From a color-table, the Michel-Levy chart, the OPD can be deduced. In cytology JL-imaging can be used as a way to determine the OPD which closely corresponds to the dry mass per area of cells in a single image. Like in other interference microscopy methods (e.g. holography), we present a phase retrieval method relying on single-shot measurements only, thus allowing real-time quantitative phase measurements. This is achieved by adding several customized 3D-printed parts (e.g. rotational polarization-filter holders) and a modern cellphone with an RGB-camera to the Jamin-Lebedeff setup, thus bringing an old microscope back to life. The algorithm is calibrated using a reference image of a known phase object (e.g. optical fiber). A gradient-descent based inverse problem generates an inverse look-up-table (LUT) which is used to convert the measured RGB signal of a phase-sample into an OPD. To account for possible ambiguities in the phase-map or phase-unwrapping artifacts we introduce a total-variation based regularization. We present results from fixed and living biological samples as well as reference samples for comparison.
Klíčová slova:
3D printing – Algorithms – Cameras – Light – Optical lenses – Polarized light microscopy – Wave interference – Interference microscopy
Zdroje
1. Bouchal P, Štrbková L, Dostál Z, Chmelík R, Bouchal Z. Geometric-Phase Microscopy for Quantitative Phase Imaging of Isotropic, Birefringent and Space-Variant Polarization Samples. Scientific Reports. 2019;9(1):3608. doi: 10.1038/s41598-019-40441-9 30837653
2. Conlon IJ, Dunn GA, Mudge AW, Raff MC. Extracellular control of cell size. Nat Cell Biol. 2001;3(10):918–921. doi: 10.1038/ncb1001-918 11584274
3. Abbe E. Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung. Archiv für mikroskopische Anatomie. 1873;9(1):413–418. doi: 10.1007/BF02956173
4. Siedentopf H, Zsigmondy R. Uber Sichtbarmachung und Größenbestimmung ultramikoskopischer Teilchen, mit besonderer Anwendung auf Goldrubingläser. Annalen der Physik. 1903;315(1):1–39. doi: 10.1002/andp.19023150102
5. Zernike F. Phase contrast, a new method for the microscopic observation of transparent objects part II. Physica. 1942;9(10):974–986. doi: 10.1016/S0031-8914(42)80079-8
6. Cogswell CJ, Libertun A, Preza C, Piestun R, King SV. Quantitative phase microscopy through differential interference imaging. Journal of Biomedical Optics. 2008. doi: 10.1117/1.2907328 18465983
7. Shribak M, Inoué S. Orientation-independent differential interference contrast microscopy. In: Microscopy and Microanalysis; 2006. doi: 10.1017/S1431927606063434
8. ZICHA D, DUNN GA. An image processing system for cell behaviour studies in subconfluent cultures. Journal of Microscopy. 1995;179(1):11–21. doi: 10.1111/j.1365-2818.1995.tb03609.x
9. Rodenburg JM. Ptychography and related diffractive imaging methods; 2008.
10. Li J, Chen Q, Zhang J, Zhang Y, Lu L, Zuo C. Highly efficient quantitative phase microscopy using programmable annular LED illumination.
11. Horstmeyer R, Yang C. A phase space model of Fourier ptychographic microscopy. Optics Express. 2014;22(1):338. doi: 10.1364/OE.22.000338 24514995
12. Collakova J, Krizova A, Kollarova V, Dostal Z, Slaba M, Vesely P, et al. Coherence-controlled holographic microscopy enabled recognition of necrosis as the mechanism of cancer cells death after exposure to cytopathic turbid emulsion. Journal of Biomedical Optics. 2015;20(11):111213. doi: 10.1117/1.JBO.20.11.111213
13. Slabý T, Kolman P, Dostál Z, Antoš M, Lošťák M, Chmelík R. Off-axis setup taking full advantage of incoherent illumination in coherence-controlled holographic microscope. Optics Express. 2013;21(12):14747. doi: 10.1364/OE.21.014747 23787662
14. Bon P, Aknoun S, Monneret S, Wattellier B. Enhanced 3D spatial resolution in quantitative phase microscopy using spatially incoherent illumination. Optics Express. 2014;22(7):8654. doi: 10.1364/OE.22.008654 24718236
15. Chanteloup JC. Multiple-wave lateral shearing interferometry for wave-front sensing. Applied Optics. 2005. doi: 10.1364/ao.44.001559 15818859
16. Popescu G, Ikeda T, Dasari RR, Feld MS. Diffraction phase microscopy for quantifying cell structure and dynamics. Optics letters. 2006;31(6):775–7. doi: 10.1364/ol.31.000775 16544620
17. Heintzmann R, Gustafsson MGL. Subdiffraction resolution in continuous samples. Nature Photonics. 2009;3(7):362–364. doi: 10.1038/nphoton.2009.102
18. van de Linde S, Löschberger A, Klein T, Heidbreder M, Wolter S, Heilemann M, et al. Direct stochastic optical reconstruction microscopy with standard fluorescent probes. Nature protocols. 2011;6(7):991–1009. doi: 10.1038/nprot.2011.336 21720313
19. Hell SW, Wichmann J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Optics Letters. 1994;19(11):780. doi: 10.1364/ol.19.000780 19844443
20. Jensen EC. Use of Fluorescent Probes: Their Effect on Cell Biology and Limitations; 2012.
21. Stavenga DG, Leertouwer HL, Wilts BD. Quantifying the refractive index dispersion of a pigmented biological tissue using Jamin-Lebedeff interference microscopy. Light: Science and Applications. 2013;2(SEPTEMBER).
22. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. 2016.
23. Wilts BDBD. Brilliant biophotonics: physical properties, pigmentary tuning & biological implications. s.n.; 2013. Available from: https://www.rug.nl/research/portal/en/publications/brilliant-biophotonics(e5c911ea-79ae-4d90-9b42-25136a2f53c8).html.
24. Seckbach JJ, Kociolek JP. The diatom world. Springer Science + Business Media; 2011.
25. Cherry RJ. New techniques of optical microscopy and microspectroscopy. CRC Press; 1991. Available from: https://www.crcpress.com/New-Techniques-of-Optical-Microscopy-and-Microspectroscopy/Cherry/p/book/9780849371172.
26. Diederich B, Marsikova B, Heintzmann R. Github Repository Jamin Lebedeff Code; 2019. Available from: https://github.com/beniroquai/Tensorflow_Jamin-Lebedeff.
27. Brown AF, Dunn GA. Microinterferometry of the movement of dry matter in fibroblasts. J Cell Sci. 1989.
28. Diekmann R, Till K, Müller M, Simonis M, Schüttpelz M, Huser T. Characterization of an industry - grade CMOS camera well suited for single molecule localization microscopy—high performance super-resolution at low cost. Scientific Reports. 2017;7(1):14425. doi: 10.1038/s41598-017-14762-6 29089524
29. Diederich B, Then P, Jügler A, Förster R, Heintzmann R. cellSTORM—Cost-effective super-resolution on a cellphone using dSTORM. PLOS ONE. 2019;14(1):e0209827. doi: 10.1371/journal.pone.0209827 30625170
30. Fuchs I. Github: FreedCam; 2018. Available from: https://github.com/KillerInk/FreeDcam.
31. Kamilov US, Mansour H, Wohlberg B. A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems. IEEE Signal Processing Letters. 2017;24(12):1872–1876. doi: 10.1109/LSP.2017.2763583
32. Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms*; 1992. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.1675&rep=rep1&type=pdf.
33. Tian L, Waller L. Quantitative differential phase contrast imaging in an LED array microscope. Optics Express. 2015;23(9):11394. doi: 10.1364/OE.23.011394 25969234
34. Horstmeyer R, Yang C. Diffraction tomography with Fourier ptychography. 2015;3(8).
35. Isola P, Zhu JY, Zhou T, Efros AA. Image-to-Image Translation with Conditional Adversarial Networks. 2016.
36. Kingma, Diederik P. and Ba, Jimmy L. Adam: A Method for Stochastic Optimization. 2014.
37. Diederich B, Marsikova B, Heintzmann R. Github Repository Jamin Lebedeff Code for ImJoy; 2019. Available from: https://github.com/bionanoimaging/UC2-ImJoy-Plugins.
38. Ouyang W, Mueller F, Hjelmare M, Lundberg E, Zimmer C. ImJoy: an open-source computational platform for the deep learning era. 1. May 2019. http://arxiv.org/abs/1905.13105. Accessed August 25, 2019.
39. FFmpeg Developers. (2016). ffmpeg tool (Version be1d324) [Software] Available from http://ffmpeg.org/
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- Může AI vyřešit nedostatek zdravotníků v Evropě?
- Jaké jsou souvislosti mezi gamblingem, depresí a pocity úniku při hře?
- „Jednohubky“ z klinického výzkumu − 2025/34
- Ukažte mi, jak kašlete, a já vám řeknu, co vám je
- Není statin jako statin aneb praktický přehled rozdílů jednotlivých molekul
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy