Abstract

We present an ultrafast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking dataset. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4, 1437–1443 (2017).
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C. T. Rueden, J. Schindelin, M. C. Hiner, B. E. DeZonia, A. E. Walter, E. T. Arena, and K. W. Eliceiri, “Imagej2: Imagej for the next generation of scientific image data,” BMC Bioinf. 18, 529 (2017).
[Crossref]

2016 (3)

S. Hugelier, J. J. De Rooi, R. Bernex, S. Duwé, O. Devos, M. Sliwa, P. Dedecker, P. H. Eilers, and C. Ruckebusch, “Sparse deconvolution of high-density super-resolution images,” Sci. Rep. 6, 21413 (2016).
[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38, 295–307 (2016).
[Crossref]

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[Crossref]

2015 (2)

J. Min, C. Vonesch, H. Kirshner, L. Carlini, N. Olivier, S. Holden, S. Manley, J. C. Ye, and M. Unser, “FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data,” Sci. Rep. 4, 4577 (2015).
[Crossref]

D. Sage, H. Kirshner, T. Pengo, N. Stuurman, J. Min, S. Manley, and M. Unser, “Quantitative evaluation of software packages for single-molecule localization microscopy,” Nat. Methods 12, 717–724 (2015).
[Crossref]

2014 (2)

M. Ovesný, P. Křížek, J. Borkovec, Z. Švindrych, and G. M. Hagen, “ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging,” Bioinformatics 30, 2389–2390 (2014).
[Crossref]

A. Barsic, G. Grover, and R. Piestun, “Three-dimensional super-resolution and localization of dense clusters of single molecules,” Sci. Rep. 4, 5388 (2014).
[Crossref]

2013 (1)

S. J. Sahl and W. Moerner, “Super-resolution fluorescence imaging with single molecules,” Curr. Opin. Struct. Biol. 23, 778–787 (2013).
[Crossref]

2012 (3)

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nat. Methods 9, 721–723 (2012).
[Crossref]

S. Cox, E. Rosten, J. Monypenny, T. Jovanovic-Talisman, D. T. Burnette, J. Lippincott-Schwartz, G. E. Jones, and R. Heintzmann, “Bayesian localization microscopy reveals nanoscale podosome dynamics,” Nat. Methods 9, 195–200 (2012).
[Crossref]

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref]

2011 (2)

F. Huang, S. L. Schwartz, J. M. Byars, and K. A. Lidke, “Simultaneous multiple-emitter fitting for single molecule super-resolution imaging,” Biomed. Opt. Express 2, 1377–1393 (2011).
[Crossref]

S. J. Holden, S. Uphoff, and A. N. Kapanidis, “DAOSTORM: an algorithm for high-density super-resolution microscopy,” Nat. Methods 8, 279–280 (2011).
[Crossref]

2009 (2)

T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. USA 106, 22287–22292 (2009).
[Crossref]

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm,” Soc. Indust. Appl. Math. J. Imaging Sci. 2, 183–202 (2009).

2008 (1)

A. Sergé, N. Bertaux, H. Rigneault, and D. Marguet, “Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes,” Nat. Methods 5, 687–694 (2008).
[Crossref]

2006 (3)

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

S. T. Hess, T. P. Girirajan, and M. D. Mason, “Ultra-high resolution imaging by fluorescence photoactivation localization microscopy,” Biophys. J. 91, 4258–4272 (2006).
[Crossref]

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3, 793–795 (2006).
[Crossref]

2004 (2)

X. Qu, D. Wu, L. Mets, and N. F. Scherer, “Nanometer-localized multiple single-molecule fluorescence microscopy,” Proc. Natl. Acad. Sci. USA 101, 11298–11303 (2004).
[Crossref]

M. P. Gordon, T. Ha, and P. R. Selvin, “Single-molecule high-resolution imaging with photobleaching,” Proc. Natl. Acad. Sci. USA 101, 6462–6465 (2004).
[Crossref]

2000 (1)

M. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. 198, 82–87 (2000).
[Crossref]

1999 (1)

1997 (1)

1994 (1)

1974 (1)

J. Högbom, “Aperture synthesis with a non-regular distribution of interferometer baselines,” Astron. Astrophys. Suppl. Ser. 15, 417–426 (1974).

1963 (1)

W. Lukosz and M. Marchand, “Optischen abbildung unter Überschreitung der beugungsbedingten auflösungsgrenze,” Opt. Acta 10, 241–255 (1963).
[Crossref]

Arena, E. T.

C. T. Rueden, J. Schindelin, M. C. Hiner, B. E. DeZonia, A. E. Walter, E. T. Arena, and K. W. Eliceiri, “Imagej2: Imagej for the next generation of scientific image data,” BMC Bioinf. 18, 529 (2017).
[Crossref]

Arganda-Carreras, I.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref]

Ashdown, G.

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7, 12471 (2016).
[Crossref]

Ba, J.

D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” arXiv:1412.6980 (2014).

Barsic, A.

A. Barsic, G. Grover, and R. Piestun, “Three-dimensional super-resolution and localization of dense clusters of single molecules,” Sci. Rep. 4, 5388 (2014).
[Crossref]

Bates, M.

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3, 793–795 (2006).
[Crossref]

Beck, A.

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm,” Soc. Indust. Appl. Math. J. Imaging Sci. 2, 183–202 (2009).

Bernex, R.

S. Hugelier, J. J. De Rooi, R. Bernex, S. Duwé, O. Devos, M. Sliwa, P. Dedecker, P. H. Eilers, and C. Ruckebusch, “Sparse deconvolution of high-density super-resolution images,” Sci. Rep. 6, 21413 (2016).
[Crossref]

Bertaux, N.

A. Sergé, N. Bertaux, H. Rigneault, and D. Marguet, “Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes,” Nat. Methods 5, 687–694 (2008).
[Crossref]

Betzig, E.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

Blanc-Féraud, L.

S. Gazagnes, E. Soubies, and L. Blanc-Féraud, “High density molecule localization for super-resolution microscopy using CEL0 based sparse approximation,” in IEEE International Symposium on Biomedical Imaging (ISBI) (2017), p. 4.

Bonifacino, J. S.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

Boothe, T.

M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, and E. W. Myers, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” bioRxiv (2017).

Borkovec, J.

M. Ovesný, P. Křížek, J. Borkovec, Z. Švindrych, and G. M. Hagen, “ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging,” Bioinformatics 30, 2389–2390 (2014).
[Crossref]

Broaddus, C.

M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, and E. W. Myers, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” bioRxiv (2017).

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241.

Burnette, D. T.

S. Cox, E. Rosten, J. Monypenny, T. Jovanovic-Talisman, D. T. Burnette, J. Lippincott-Schwartz, G. E. Jones, and R. Heintzmann, “Bayesian localization microscopy reveals nanoscale podosome dynamics,” Nat. Methods 9, 195–200 (2012).
[Crossref]

Byars, J. M.

Cardona, A.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref]

Carlini, L.

J. Min, C. Vonesch, H. Kirshner, L. Carlini, N. Olivier, S. Holden, S. Manley, J. C. Ye, and M. Unser, “FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data,” Sci. Rep. 4, 4577 (2015).
[Crossref]

Colyer, R.

T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. USA 106, 22287–22292 (2009).
[Crossref]

Cox, S.

S. Cox, E. Rosten, J. Monypenny, T. Jovanovic-Talisman, D. T. Burnette, J. Lippincott-Schwartz, G. E. Jones, and R. Heintzmann, “Bayesian localization microscopy reveals nanoscale podosome dynamics,” Nat. Methods 9, 195–200 (2012).
[Crossref]

Culley, S.

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira, and R. Henriques, “Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations,” Nat. Commun. 7, 12471 (2016).
[Crossref]

M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, and E. W. Myers, “Content-aware image restoration: pushing the limits of fluorescence microscopy,” bioRxiv (2017).

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Davidson, M. W.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313, 1642–1645 (2006).
[Crossref]

De Rooi, J. J.

S. Hugelier, J. J. De Rooi, R. Bernex, S. Duwé, O. Devos, M. Sliwa, P. Dedecker, P. H. Eilers, and C. Ruckebusch, “Sparse deconvolution of high-density super-resolution images,” Sci. Rep. 6, 21413 (2016).
[Crossref]

Dedecker, P.

S. Hugelier, J. J. De Rooi, R. Bernex, S. Duwé, O. Devos, M. Sliwa, P. Dedecker, P. H. Eilers, and C. Ruckebusch, “Sparse deconvolution of high-density super-resolution images,” Sci. Rep. 6, 21413 (2016).
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Supplementary Material (1)

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» Supplement 1       Supplemental information including network architecture, training information, performance evaluation and further comparisons.

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Figures (7)

Fig. 1.
Fig. 1. Network architecture. A set of low-resolution diffraction-limited images of stochastically blinking emitters is fed into the network to produce reconstructed high-resolution images. The resulting outputs are then summed to generate the final super-resolved image.
Fig. 2.
Fig. 2. Simulated dense emitters. (a) Low-resolution image. Scale bar is 0.5 μm. (b) Deep-STORM prediction on a 12.5 nm grid with ground truth emitter locations overlaid as cross marks on top.
Fig. 3.
Fig. 3. Resolution and emitter density (simulation). (a) Diffraction-limited image of horizontal lines. Scale bar is 500 nm. (b) Simulated single frames of emitters at various densities with a mean of 10 background photons per pixel and 1000 signal photons per emitter. (c) The ground truth positions of simulated emitters. (d) Deep-STORM reconstructed images. (e) Sum along the horizontal axis of the reconstruction intensities.
Fig. 4.
Fig. 4. Simulated microtubules. (a) Sum of the acquisition stack. Scale bar is 1 μm. (b) Ground truth. (c) Reconstruction by the CEL0 method (d) Reconstruction by Deep-STORM. (e), (f) Magnified views of two selected regions. Scale bars are 0.5 μm.
Fig. 5.
Fig. 5. Reconstruction accuracy. (a) Ground truth image of simulated microtubules. Scale bar is 1 μm. (b) Merged reconstruction with the ground truth. Red shows the ground truth, green corresponds to the recovery result, and yellow marks their overlap. Note that CEL0 (left) does not follow the twisted shape in all places (white triangles), while Deep-STORM (right) better recovers the underlying structure.
Fig. 6.
Fig. 6. Experimentally measured microtubules. (a) Sum of the acquisition stack. Scale bar is 2 μm. (b) Reconstruction by the CEL0 method. (c) Reconstruction by Deep-STORM. (d), (e) Magnified views of two selected regions. Scale bars are 0.5 μm. (f) The width projection of the highlighted yellow region. The attained FWHM (black triangles) for CEL0 was 61 nm and 67 nm for Deep-STORM. The black line shows the diffraction-limited projection.
Fig. 7.
Fig. 7. Quantum dot experimental data. (a) Acquired low-resolution image. Scale bar is 1 μm. (b) Deep-STORM reconstruction with ground truth emitter positions (red crosses). (c) Magnified view of the selected region in (b).

Tables (1)

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Table 1. Runtime Comparison

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(x,x^)=1NΣi=1Nx^igxig22+x^i1.

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