Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Deconvolution microscopy is a computational image-processing technique used in conjunction with fluorescence microscopy to increase the resolution and contrast of three-dimensional images. Fluorescence microscopy is a widely used technique in biology and medicine that involves labeling specific molecules or structures within a sample with fluorescent dyes and then electronically photographing the sample through a microscope. However, the resolution of conventional fluorescence microscopy is limited by diffraction within the microscope’s optical path, which causes blurring of the image and reduces the ability to resolve structures in close proximity with one another. Deconvolution microscopy overcomes this limitation by means of computer-based image processing whereby mathematical algorithms are used to eliminate the blurring caused by the microscope’s optics and thus obtain a higher-resolution image that reveals the fine details of the sample with greater accuracy. Deconvolution microscopy, which can be applied to a range of image acquisition modalities, including widefield, confocal, and super-resolution microscopy, has become an essential tool for studying the structure and function of biological systems at the cellular and molecular levels. In this perspective, the latest deconvolution techniques have been introduced and image-processing methods for medical purposes have been presented.

© 2023 Bentham Science Publishers
© 2023 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cmir/10.2174/1573405620666230602123028
2024-01-01
2024-11-23

  • From This Site

    /content/journals/cmir/10.2174/1573405620666230602123028
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/CMIR-20-E020623217605.html?itemId=/content/journals/cmir/10.2174/1573405620666230602123028&mimeType=html&fmt=ahah

References

  1. McNallyJ.G. KarpovaT. CooperJ. ConchelloJ.A. Three-dimensional imaging by deconvolution microscopy.Methods199919337338510.1006/meth.1999.087310579932
     [Google Scholar]
  2. SibaritaJ.B. Deconvolution microscopy.Adv. Biochem. Eng. Biotechnol.20059520124310.1007/b10221516080270
     [Google Scholar]
  3. RichardsonW.H. Bayesian-based iterative method of image restoration.J. Opt. Soc. Am.1972621555910.1364/JOSA.62.000055
     [Google Scholar]
  4. AgardD.A. HiraokaY. ShawP. SedatJ.W. Fluorescence microscopy in three dimensions.Methods Cell Biol.19893035337710.1016/S0091‑679X(08)60986‑32494418
     [Google Scholar]
  5. SwedlowJ.R. Quantitative fluorescence microscopy and image deconvolution.Methods Cell Biol.201311440742610.1016/B978‑0‑12‑407761‑4.00017‑823931516
     [Google Scholar]
  6. GokhinD.S. FowlerV.M. Software-based measurement of thin filament lengths: An open-source GUI for Distributed Deconvolution analysis of fluorescence images.J. Microsc.20172651112010.1111/jmi.1245627644080
     [Google Scholar]
  7. KatohK. Software-based three-dimensional deconvolution microscopy of cytoskeletal proteins in cultured fibroblast using open-source software and open hardware.J. Imaging20195128810.3390/jimaging512008834460602
     [Google Scholar]
  8. KubalováI. NěmečkováA. WeisshartK. HřibováE. SchubertV. Comparing super-resolution microscopy techniques to analyze chromosomes.Int. J. Mol. Sci.2021224190310.3390/ijms2204190333672992
     [Google Scholar]
  9. PrigentS. NguyenH.N. LeconteL. Valades-CruzC.A. HajjB. SalameroJ. KervrannC. SPITFIR(e): A supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos.Sci. Rep.2023131148910.1038/s41598‑022‑26178‑y36707688
     [Google Scholar]
  10. LiaoH. SheridanT. CosarE. OwensC. ZuoT. WangX. AkalinA. KandilD. DresserK. FogartyK. BellveK. BaerC. FischerA. Deconvolution microscopy: A platform for rapid on‐site evaluation of fine needle aspiration specimens that enables recovery of the sample.Cytopathology202233331232010.1111/cyt.1310635102620
     [Google Scholar]
  11. GuoM. LiY. SuY. LambertT. NogareD.D. MoyleM.W. DuncanL.H. IkegamiR. SantellaA. Rey-SuarezI. GreenD. BeirigerA. ChenJ. VishwasraoH. GanesanS. PrinceV. WatersJ.C. AnnunziataC.M. HafnerM. MohlerW.A. ChitnisA.B. UpadhyayaA. UsdinT.B. BaoZ. Colón-RamosD. La RiviereP. LiuH. WuY. ShroffH. Rapid image deconvolution and multiview fusion for optical microscopy.Nat. Biotechnol.202038111337134610.1038/s41587‑020‑0560‑x32601431
     [Google Scholar]
  12. KimB. DVDeconv: An open-source MATLAB toolbox for depth-variant asymmetric deconvolution of fluorescence micrographs.Cells202110239710.3390/cells1002039733671933
     [Google Scholar]
  13. BeckerK. SaghafiS. PendeM. Sabdyusheva-LitschauerI. HahnC.M. ForoughipourM. JährlingN. DodtH.U. Deconvolution of light sheet microscopy recordings.Sci. Rep.2019911762510.1038/s41598‑019‑53875‑y31772375
     [Google Scholar]
  14. CorbettaE. CandeoA. BassiA. AncoraD. Blind deconvolution in autocorrelation inversion for multiview light‐sheet microscopy.Microsc. Res. Tech.20228562282229110.1002/jemt.2408535199902
     [Google Scholar]
  15. SchneiderC.A. RasbandW.S. EliceiriK.W. NIH Image to ImageJ: 25 years of image analysis.Nat. Methods20129767167510.1038/nmeth.208922930834
     [Google Scholar]
  16. EdelsteinA. AmodajN. HooverK. ValeR. StuurmanN. Computer control of microscopes using µManager.Curr Protoc Mol Biol.201014.20.
     [Google Scholar]
  17. SageD. DonatiL. SoulezF. FortunD. SchmitG. SeitzA. GuietR. VoneschC. UnserM. DeconvolutionLab2: An open-source software for deconvolution microscopy.Methods2017115284110.1016/j.ymeth.2016.12.01528057586
     [Google Scholar]
  18. DoughertyR. 11Th AAA/CEAS Aeroacoustics ConterenceMonterey, California23 May 200520052005296110.2514/6.2005‑2961
     [Google Scholar]
  19. KirshnerH. AguetF. SageD. UnserM. 3-D PSF fitting for fluorescence microscopy: Implementation and localization application.J. Microsc.20132491132510.1111/j.1365‑2818.2012.03675.x23126323
     [Google Scholar]
  20. ManleyS. GilletteJ.M. PattersonG.H. ShroffH. HessH.F. BetzigE. Lippincott-SchwartzJ. High-density mapping of single-molecule trajectories with photoactivated localization microscopy.Nat. Methods20085215515710.1038/nmeth.117618193054
     [Google Scholar]
  21. HeddeP.N. FuchsJ. OswaldF. WiedenmannJ. NienhausG.U. Online image analysis software for photoactivation localization microscopy.Nat. Methods200961068969010.1038/nmeth1009‑68919789527
     [Google Scholar]
  22. MärkiI. BocchioN.L. GeissbuehlerS. AguetF. BilencaA. LasserT. Three-dimensional nano-localization of single fluorescent emitters.Opt. Express20101819202632027210.1364/OE.18.02026320940917
     [Google Scholar]
  23. GeissbuehlerS. DellagiacomaC. LasserT. Comparison between SOFI and STORM.Biomed. Opt. Express20112340842010.1364/BOE.2.00040821412447
     [Google Scholar]
  24. LaneR.G. Methods for maximum-likelihood deconvolution.J. Opt. Soc. Am. A Opt. Image Sci. Vis.199613101992199810.1364/JOSAA.13.001992
     [Google Scholar]
  25. LamE.Y. GoodmanJ.W. Iterative statistical approach to blind image deconvolution.J. Opt. Soc. Am. A Opt. Image Sci. Vis.20001771177118410.1364/JOSAA.17.00117710883969
     [Google Scholar]
  26. LiliacI.M. UngureanuB.S. MărgăritescuC. SacerdoțianuV.M. SăftoiuA. MogoantăL. MoraruE. PiriciD. E-cadherin modulation and inter-cellular trafficking in tubular gastric adenocarcinoma: A high-resolution microscopy pilot study.Biomedicines202210234910.3390/biomedicines1002034935203558
     [Google Scholar]
  27. LeeT.Y. LuW.J. ChangouC.A. HsiungY.C. TrangN.T.T. LeeC.Y. ChangT.H. JayakumarT. HsiehC.Y. YangC.H. ChangC.C. ChenR.J. SheuJ.R. LinK.H. Platelet autophagic machinery involved in thrombosis through a novel linkage of AMPK-MTOR to sphingolipid metabolism.Autophagy202117124141415810.1080/15548627.2021.190449533749503
     [Google Scholar]
  28. Xypakis, E.; Gosti, G.; Giordani, T.; Santagati, R.; Ruocco, G.; Leonetti, MDeep learning for blind structured illumination microscopy.Sci Rep2022121862310.1038/s41598‑022‑12571‑0
     [Google Scholar]
  29. de MonvelB.J. Le CalvezS. UlfendahlM. Image restoration for confocal microscopy: Improving the limits of deconvolution, with application to the visualization of the mammalian hearing organ.Biophys. J.20018052455247010.1016/S0006‑3495(01)76214‑511325744
     [Google Scholar]
  30. HeT. SunY. QiJ. HuJ. HuangH. Image deconvolution for confocal laser scanning microscopy using constrained total variation with a gradient field.Appl. Opt.201958143754376610.1364/AO.58.00375431158185
     [Google Scholar]
/content/journals/cmir/10.2174/1573405620666230602123028
Loading
Recent Applications of Deconvolution Microscopy in Medicine
Curr. Med. Imaging 20, e020623217605 (2024); https://doi.org/10.2174/1573405620666230602123028
/content/journals/cmir/10.2174/1573405620666230602123028
/content/journals/cmir/10.2174/1573405620666230602123028
Loading

Data & Media loading...


  • Article Type:     Editorial
Keyword(s): Cell biology; Confocal; Deconvolution microscopy; Diffraction; Fluorescent microscopy; Medical imaging

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test