Abstract

Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning-based molecule density estimator that is accurate over an extended range of densities. This system obviates the need to fine-tune parameters and enables robust, autonomous illumination control for localization microscopy.

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

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2018 (3)

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283–292 (2018).
[Crossref]

S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
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E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5(4), 458–464 (2018).
[Crossref]

2017 (8)

L. He, X. Ren, Q. Gao, X. Zhao, B. Yao, and Y. Chao, “The connected-component labeling problem: A review of state-of-the-art algorithms,” Pattern Recognit. 70, 25–43 (2017).
[Crossref]

J. Li, F. Xue, and T. Blu, “Fast and accurate three-dimensional point spread function computation for fluorescence microscopy,” J. Opt. Soc. Am. A 34(6), 1029–1034 (2017).
[Crossref] [PubMed]

R. Diekmann, Ø. I. Helle, C. I. Øie, P. McCourt, T. R. Huser, M. Schüttpelz, and B. S. Ahluwalia, “Chip-based wide field-of-view nanoscopy,” Nat. Photonics 11(5), 322–328 (2017).
[Crossref]

Z. Zhao, B. Xin, L. Li, and Z.-L. Huang, “High-power homogeneous illumination for super-resolution localization microscopy with large field-of-view,” Opt. Express 25(12), 13382–13395 (2017).
[Crossref] [PubMed]

M. Mund, J. A. van der Beek, J. Deschamps, S. Dmitrieff, P. Hoess, J. L. Monster, A. Picco, F. Nédélec, M. Kaksonen, and J. Ries, “Systematic analysis of the molecular architecture of endocytosis reveals a nanoscale actin nucleation template that drives efficient vesicle formation,” Cell 174, 884–896 (2017).
[Crossref] [PubMed]

A. Beghin, A. Kechkar, C. Butler, F. Levet, M. Cabillic, O. Rossier, G. Giannone, R. Galland, D. Choquet, and J.-B. Sibarita, “Localization-based super-resolution imaging meets high-content screening,” Nat. Methods 14(12), 1184–1190 (2017).
[Crossref] [PubMed]

P. Fox-Roberts, R. Marsh, K. Pfisterer, A. Jayo, M. Parsons, and S. Cox, “Local dimensionality determines imaging speed in localization microscopy,” Nat. Commun. 8, 13558 (2017).
[Crossref] [PubMed]

J.-F. Rupprecht, A. Martinez-Marrades, Z. Zhang, R. Changede, P. Kanchanawong, and G. Tessier, “Trade-offs between structural integrity and acquisition time in stochastic super-resolution microscopy techniques,” Opt. Express 25(19), 23146–23163 (2017).
[Crossref] [PubMed]

2016 (2)

K. M. Douglass, C. Sieben, A. Archetti, A. Lambert, and S. Manley, “Super-resolution imaging of multiple cells by optimised flat-field epi-illumination,” Nat. Photonics 10(11), 705–708 (2016).
[Crossref] [PubMed]

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] [PubMed]

2015 (3)

Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
[Crossref] [PubMed]

A. Burgert, S. Letschert, S. Doose, and M. Sauer, “Artifacts in single-molecule localization microscopy,” Histochem. Cell Biol. 144(2), 123–131 (2015).
[Crossref] [PubMed]

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(8), 717–724 (2015).
[Crossref] [PubMed]

2014 (4)

S. J. Holden, T. Pengo, K. L. Meibom, C. Fernandez Fernandez, J. Collier, and S. Manley, “High throughput 3D super-resolution microscopy reveals Caulobacter crescentus in vivo Z-ring organization,” Proc. Natl. Acad. Sci. U.S.A. 111(12), 4566–4571 (2014).
[Crossref] [PubMed]

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1(2), 10 (2014).
[Crossref] [PubMed]

M. Ovesný, P. Křížek, J. Borkovec, Z. Svindrych, and G. M. Hagen, “ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging,” Bioinformatics 30(16), 2389–2390 (2014).
[Crossref] [PubMed]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

2013 (3)

R. P. J. Nieuwenhuizen, K. A. Lidke, M. Bates, D. L. Puig, D. Grünwald, S. Stallinga, and B. Rieger, “Measuring image resolution in optical nanoscopy,” Nat. Methods 10(6), 557–562 (2013).
[Crossref] [PubMed]

N. Banterle, K. H. Bui, E. A. Lemke, and M. Beck, “Fourier ring correlation as a resolution criterion for super-resolution microscopy,” J. Struct. Biol. 183(3), 363–367 (2013).
[Crossref] [PubMed]

A. Kechkar, D. Nair, M. Heilemann, D. Choquet, and J.-B. Sibarita, “Real-time analysis and visualization for single-molecule based super-resolution microscopy,” PLoS One 8(4), e62918 (2013).
[Crossref] [PubMed]

2012 (1)

2009 (1)

T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. U.S.A. 106(52), 22287–22292 (2009).
[Crossref] [PubMed]

2008 (1)

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
[Crossref] [PubMed]

2006 (3)

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3(10), 793–796 (2006).
[Crossref] [PubMed]

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(5793), 1642–1645 (2006).
[Crossref] [PubMed]

S. T. Hess, T. P. K. Girirajan, and M. D. Mason, “Ultra-high resolution imaging by fluorescence photoactivation localization microscopy,” Biophys. J. 91(11), 4258–4272 (2006).
[Crossref] [PubMed]

2005 (2)

D. Sage, F. R. Neumann, F. Hediger, S. M. Gasser, and M. Unser, “Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics,” IEEE Trans. Image Process. 14(9), 1372–1383 (2005).
[Crossref] [PubMed]

J. Bechhoefer, “Feedback for physicists: A tutorial essay on control,” Rev. Mod. Phys. 77(3), 783–836 (2005).
[Crossref]

1992 (1)

1991 (1)

C. Allain and M. Cloitre, “Characterizing the lacunarity of random and deterministic fractal sets,” Phys. Rev. A 44(6), 3552–3558 (1991).
[Crossref] [PubMed]

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Ahluwalia, B. S.

R. Diekmann, Ø. I. Helle, C. I. Øie, P. McCourt, T. R. Huser, M. Schüttpelz, and B. S. Ahluwalia, “Chip-based wide field-of-view nanoscopy,” Nat. Photonics 11(5), 322–328 (2017).
[Crossref]

Albrecht, D.

S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
[Crossref] [PubMed]

Allain, C.

C. Allain and M. Cloitre, “Characterizing the lacunarity of random and deterministic fractal sets,” Phys. Rev. A 44(6), 3552–3558 (1991).
[Crossref] [PubMed]

Amodaj, N.

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1(2), 10 (2014).
[Crossref] [PubMed]

Archetti, A.

K. M. Douglass, C. Sieben, A. Archetti, A. Lambert, and S. Manley, “Super-resolution imaging of multiple cells by optimised flat-field epi-illumination,” Nat. Photonics 10(11), 705–708 (2016).
[Crossref] [PubMed]

Arteta, C.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European Conference on Computer Vision – ECCV (Springer, 2014), pp. 504–518.

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] [PubMed]

Banterle, N.

N. Banterle, K. H. Bui, E. A. Lemke, and M. Beck, “Fourier ring correlation as a resolution criterion for super-resolution microscopy,” J. Struct. Biol. 183(3), 363–367 (2013).
[Crossref] [PubMed]

Barham, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Bates, M.

R. P. J. Nieuwenhuizen, K. A. Lidke, M. Bates, D. L. Puig, D. Grünwald, S. Stallinga, and B. Rieger, “Measuring image resolution in optical nanoscopy,” Nat. Methods 10(6), 557–562 (2013).
[Crossref] [PubMed]

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3(10), 793–796 (2006).
[Crossref] [PubMed]

Bechhoefer, J.

J. Bechhoefer, “Feedback for physicists: A tutorial essay on control,” Rev. Mod. Phys. 77(3), 783–836 (2005).
[Crossref]

Beck, M.

N. Banterle, K. H. Bui, E. A. Lemke, and M. Beck, “Fourier ring correlation as a resolution criterion for super-resolution microscopy,” J. Struct. Biol. 183(3), 363–367 (2013).
[Crossref] [PubMed]

Beghin, A.

A. Beghin, A. Kechkar, C. Butler, F. Levet, M. Cabillic, O. Rossier, G. Giannone, R. Galland, D. Choquet, and J.-B. Sibarita, “Localization-based super-resolution imaging meets high-content screening,” Nat. Methods 14(12), 1184–1190 (2017).
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Betzig, E.

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L. He, X. Ren, Q. Gao, X. Zhao, B. Yao, and Y. Chao, “The connected-component labeling problem: A review of state-of-the-art algorithms,” Pattern Recognit. 70, 25–43 (2017).
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S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

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T. Dertinger, R. Colyer, G. Iyer, S. Weiss, and J. Enderlein, “Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI),” Proc. Natl. Acad. Sci. U.S.A. 106(52), 22287–22292 (2009).
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Jacobs, C.

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P. Fox-Roberts, R. Marsh, K. Pfisterer, A. Jayo, M. Parsons, and S. Cox, “Local dimensionality determines imaging speed in localization microscopy,” Nat. Commun. 8, 13558 (2017).
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Kasper, R.

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M. Ovesný, P. Křížek, J. Borkovec, Z. Svindrych, and G. M. Hagen, “ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging,” Bioinformatics 30(16), 2389–2390 (2014).
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

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K. M. Douglass, C. Sieben, A. Archetti, A. Lambert, and S. Manley, “Super-resolution imaging of multiple cells by optimised flat-field epi-illumination,” Nat. Photonics 10(11), 705–708 (2016).
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S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
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A. Burgert, S. Letschert, S. Doose, and M. Sauer, “Artifacts in single-molecule localization microscopy,” Histochem. Cell Biol. 144(2), 123–131 (2015).
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A. Beghin, A. Kechkar, C. Butler, F. Levet, M. Cabillic, O. Rossier, G. Giannone, R. Galland, D. Choquet, and J.-B. Sibarita, “Localization-based super-resolution imaging meets high-content screening,” Nat. Methods 14(12), 1184–1190 (2017).
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Li, L.

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K. M. Douglass, C. Sieben, A. Archetti, A. Lambert, and S. Manley, “Super-resolution imaging of multiple cells by optimised flat-field epi-illumination,” Nat. Photonics 10(11), 705–708 (2016).
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Min, J.

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(8), 717–724 (2015).
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

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M. Mund, J. A. van der Beek, J. Deschamps, S. Dmitrieff, P. Hoess, J. L. Monster, A. Picco, F. Nédélec, M. Kaksonen, and J. Ries, “Systematic analysis of the molecular architecture of endocytosis reveals a nanoscale actin nucleation template that drives efficient vesicle formation,” Cell 174, 884–896 (2017).
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Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

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M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
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A. Kechkar, D. Nair, M. Heilemann, D. Choquet, and J.-B. Sibarita, “Real-time analysis and visualization for single-molecule based super-resolution microscopy,” PLoS One 8(4), e62918 (2013).
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L. Fiaschi, U. Koethe, R. Nair, and F. A. Hamprecht, “Learning to count with regression forest and structured labels,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR) (2012), pp. 2685–2688.

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M. Mund, J. A. van der Beek, J. Deschamps, S. Dmitrieff, P. Hoess, J. L. Monster, A. Picco, F. Nédélec, M. Kaksonen, and J. Ries, “Systematic analysis of the molecular architecture of endocytosis reveals a nanoscale actin nucleation template that drives efficient vesicle formation,” Cell 174, 884–896 (2017).
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R. Diekmann, Ø. I. Helle, C. I. Øie, P. McCourt, T. R. Huser, M. Schüttpelz, and B. S. Ahluwalia, “Chip-based wide field-of-view nanoscopy,” Nat. Photonics 11(5), 322–328 (2017).
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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(5793), 1642–1645 (2006).
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D. Oñoro-Rubio and R. J. López-Sastre, “Towards perspective-free object counting with deep learning,” in European Conference on Computer Vision (ECCV) (Springer, 2016), pp. 615–629.
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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(5793), 1642–1645 (2006).
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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(8), 717–724 (2015).
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S. Culley, D. Albrecht, C. Jacobs, P. M. Pereira, C. Leterrier, J. Mercer, and R. Henriques, “Quantitative mapping and minimization of super-resolution optical imaging artifacts,” Nat. Methods 15(4), 263–266 (2018).
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P. Fox-Roberts, R. Marsh, K. Pfisterer, A. Jayo, M. Parsons, and S. Cox, “Local dimensionality determines imaging speed in localization microscopy,” Nat. Commun. 8, 13558 (2017).
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M. Mund, J. A. van der Beek, J. Deschamps, S. Dmitrieff, P. Hoess, J. L. Monster, A. Picco, F. Nédélec, M. Kaksonen, and J. Ries, “Systematic analysis of the molecular architecture of endocytosis reveals a nanoscale actin nucleation template that drives efficient vesicle formation,” Cell 174, 884–896 (2017).
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Racine, V.

Rebane, A. A.

Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
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M. Mund, J. A. van der Beek, J. Deschamps, S. Dmitrieff, P. Hoess, J. L. Monster, A. Picco, F. Nédélec, M. Kaksonen, and J. Ries, “Systematic analysis of the molecular architecture of endocytosis reveals a nanoscale actin nucleation template that drives efficient vesicle formation,” Cell 174, 884–896 (2017).
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A. Beghin, A. Kechkar, C. Butler, F. Levet, M. Cabillic, O. Rossier, G. Giannone, R. Galland, D. Choquet, and J.-B. Sibarita, “Localization-based super-resolution imaging meets high-content screening,” Nat. Methods 14(12), 1184–1190 (2017).
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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(8), 717–724 (2015).
[Crossref] [PubMed]

D. Sage, F. R. Neumann, F. Hediger, S. M. Gasser, and M. Unser, “Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics,” IEEE Trans. Image Process. 14(9), 1372–1383 (2005).
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Sauer, M.

A. Burgert, S. Letschert, S. Doose, and M. Sauer, “Artifacts in single-molecule localization microscopy,” Histochem. Cell Biol. 144(2), 123–131 (2015).
[Crossref] [PubMed]

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
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Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
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R. Diekmann, Ø. I. Helle, C. I. Øie, P. McCourt, T. R. Huser, M. Schüttpelz, and B. S. Ahluwalia, “Chip-based wide field-of-view nanoscopy,” Nat. Photonics 11(5), 322–328 (2017).
[Crossref]

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
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Seefeldt, B.

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
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Shechtman, Y.

Sibarita, J. B.

Sibarita, J.-B.

A. Beghin, A. Kechkar, C. Butler, F. Levet, M. Cabillic, O. Rossier, G. Giannone, R. Galland, D. Choquet, and J.-B. Sibarita, “Localization-based super-resolution imaging meets high-content screening,” Nat. Methods 14(12), 1184–1190 (2017).
[Crossref] [PubMed]

A. Kechkar, D. Nair, M. Heilemann, D. Choquet, and J.-B. Sibarita, “Real-time analysis and visualization for single-molecule based super-resolution microscopy,” PLoS One 8(4), e62918 (2013).
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K. M. Douglass, C. Sieben, A. Archetti, A. Lambert, and S. Manley, “Super-resolution imaging of multiple cells by optimised flat-field epi-illumination,” Nat. Photonics 10(11), 705–708 (2016).
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Sougrat, R.

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(5793), 1642–1645 (2006).
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Specht, C. G.

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Stallinga, S.

R. P. J. Nieuwenhuizen, K. A. Lidke, M. Bates, D. L. Puig, D. Grünwald, S. Stallinga, and B. Rieger, “Measuring image resolution in optical nanoscopy,” Nat. Methods 10(6), 557–562 (2013).
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Steiner, B.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Stuurman, N.

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(8), 717–724 (2015).
[Crossref] [PubMed]

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1(2), 10 (2014).
[Crossref] [PubMed]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Svindrych, Z.

M. Ovesný, P. Křížek, J. Borkovec, Z. Svindrych, and G. M. Hagen, “ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging,” Bioinformatics 30(16), 2389–2390 (2014).
[Crossref] [PubMed]

Tessier, G.

Tinnefeld, P.

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
[Crossref] [PubMed]

Triller, A.

Tsuchida, M. A.

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1(2), 10 (2014).
[Crossref] [PubMed]

Tucker, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Unser, M.

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(8), 717–724 (2015).
[Crossref] [PubMed]

D. Sage, F. R. Neumann, F. Hediger, S. M. Gasser, and M. Unser, “Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics,” IEEE Trans. Image Process. 14(9), 1372–1383 (2005).
[Crossref] [PubMed]

Vale, R. D.

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1(2), 10 (2014).
[Crossref] [PubMed]

van de Linde, S.

M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
[Crossref] [PubMed]

van der Beek, J. A.

M. Mund, J. A. van der Beek, J. Deschamps, S. Dmitrieff, P. Hoess, J. L. Monster, A. Picco, F. Nédélec, M. Kaksonen, and J. Ries, “Systematic analysis of the molecular architecture of endocytosis reveals a nanoscale actin nucleation template that drives efficient vesicle formation,” Cell 174, 884–896 (2017).
[Crossref] [PubMed]

Vasudevan, V.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Velasco, M. G. M.

Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
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Virrueta, A.

Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
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Warden, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Xie, W.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283–292 (2018).
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Xue, F.

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Yu, Y.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Zhang, Y.

Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
[Crossref] [PubMed]

Y. Lin, J. J. Long, F. Huang, W. C. Duim, S. Kirschbaum, Y. Zhang, L. K. Schroeder, A. A. Rebane, M. G. M. Velasco, A. Virrueta, D. W. Moonan, J. Jiao, S. Y. Hernandez, Y. Zhang, and J. Bewersdorf, “Quantifying and optimizing single-molecule switching nanoscopy at high speeds,” PLoS One 10(5), e0128135 (2015).
[Crossref] [PubMed]

Zhang, Z.

Zhao, X.

L. He, X. Ren, Q. Gao, X. Zhao, B. Yao, and Y. Chao, “The connected-component labeling problem: A review of state-of-the-art algorithms,” Pattern Recognit. 70, 25–43 (2017).
[Crossref]

Zhao, Z.

Zheng, X.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, and G. Brain, “TensorFlow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16) (2016), pp. 265–284.

Zhuang, X.

M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM),” Nat. Methods 3(10), 793–796 (2006).
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Zisserman, A.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283–292 (2018).
[Crossref]

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Interactive object counting,” in European Conference on Computer Vision – ECCV (Springer, 2014), pp. 504–518.

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M. Heilemann, S. van de Linde, M. Schüttpelz, R. Kasper, B. Seefeldt, A. Mukherjee, P. Tinnefeld, and M. Sauer, “Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes,” Angew. Chem. Int. Ed. Engl. 47(33), 6172–6176 (2008).
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W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(3), 283–292 (2018).
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A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1(2), 10 (2014).
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

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Supplementary Material (1)

NameDescription
» Visualization 1       An example of how the self-tuning PI controller calibrates itself during an automated STORM image sequence. Fluorescent spots were counted using a fully convolutional neural network called DEFCoN.

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Figures (10)

Fig. 1
Fig. 1 The autonomous illumination control system. The three primary components in the feedback loop are represented as modular blocks, and the data passed between the components are indicated in italics.
Fig. 2
Fig. 2 Bias in the detection of fluorescent spots. a) A single image from a simulated PALM acquisition demonstrates two types of counting errors: undercounting due to poor signal-to-noise (left arrow) and undercounting due to overlapping PSFs (right arrow). Red x’s: ground truth emitters; cyan circles: detected molecules using a wavelet matched filter. Scale bar: 1 µm. b) The number of fluorescence spots detected by the wavelet/watershed algorithm of [21] using different values for the B-spline scale parameters [22] vs. the true number of emitting fluorophores in images from a simulated PALM data set. The gray line indicates an unbiased result. Data points are binned averages with error bars representing the 95% confidence interval of the mean.
Fig. 3
Fig. 3 Density map estimation for fluorescence spot counting. a) A target density map generated from ground truth simulated data. The integral over the density map is the number of fluorescent spots in the FOV. Red x’s denote ground truth positions. b) The architecture of DEFCoN. (De)Conv.: (de)convolutional layer. ReLU: rectified linear units. The number of convolution kernels (and subsequent strided convolution kernels) used in each layer is indicated by the number below. For example, the first layer of the segmentation network is composed of 16 convolution kernels, each 3-by-3 in size, followed by ReLU activation, followed by 16 strided 3-by-3 convolutions, followed by ReLU.
Fig. 4
Fig. 4 Training DEFCoN’s neural networks. The training takes place in two steps: first the segmentation network alone is trained on target segmentation maps. Then its weights are frozen and the full network is trained on the target density maps.
Fig. 5
Fig. 5 Comparison between DEFCoN and the wavelet filtering/watershed method from [21]. a) The mean counting error’s dependence on the density of randomly distributed fluorophores. The magenta tick indicates the density in the simulated data sets for panel b. b) The dependence of the error on the SNR. The magenta tick indicates the SNR of the simulated data sets in panel a.
Fig. 6
Fig. 6 Execution times for DEFCoN and wavelet-based segmentation. The dependence of the execution time on the image size scales linearly with the number of pixels.
Fig. 7
Fig. 7 a) A proportional-integral (PI) controller. b) The number of detected localizations per frame in an acquisition where the UV laser power was controlled by the PI controller. c) The number of detected localizations per frame where the UV laser was manually adjusted.
Fig. 8
Fig. 8 Construction of the self-tuning procedure for the PI controller.
Fig. 9
Fig. 9 The local density estimates are made by computing the sum of the pixels in each subregion of a density map. (In this example, the size of the subregions is 7 x 7 pixels; an example is the red square on the left.) The maximum local count (the red square on the right) is the largest value found in the map of local density estimates.
Fig. 10
Fig. 10 The average maximum local count (AMLC) as a heuristic for set point determination. a) The AMLC value directly determines the tradeoff between the degree of artifacts in the SMLM image (precision and recall) and the rate at which localizations are detected. b) SMLM images of a simulated microtubule network acquired at different AMLC values. AMLC values are in the upper left corner of each image. False positive localizations are in red. Scale bar: 1 µm.

Tables (1)

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Table 1 Mean counting errors on real data sets.

Equations (9)

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l= l pixel +γ l count
l pixel = i,j ( d ^ i,j d i,j ) 2
l count = ( i,j d ^ i,j i,j d i,j ) 2
I i,j ' = I i,j min( I i,j ) max( I i,j )min( I i,j )
CE= | N ^ N | N
P( t )= K p e( t )+ K i 0 t e( t' )dt'
K p = ΔP ΔN τ ( λ+ t d )
K i = K p τ 1
MLC= max sS ( ( i,j )s d ^ i,j )

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