Abstract

Current optical-sectioning methods require complex optical system or considerable computation time to improve imaging quality. Here we propose a deep learning-based method for optical sectioning of wide-field images. This method only needs one pair of contrast images for training to facilitate reconstruction of an optically sectioned image. The removal effect of background information and resolution that is achievable with our technique is similar to traditional optical-sectioning methods, but offers lower noise levels and a higher imaging depth. Moreover, reconstruction speed can be optimized to 14 Hz. This cost-effective and convenient method enables high-throughput optical sectioning techniques to be developed.

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

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References

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2018 (7)

Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Günaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5(6), 704–710 (2018).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7(2), 17141 (2018).
[Crossref]

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

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]

T. Nguyen, Y. Xue, Y. Li, L. Tian, and G. Nehmetallah, “Deep learning approach for Fourier ptychography microscopy,” Opt. Express 26(20), 26470–26484 (2018).
[Crossref]

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

X. Chen, X. Zhang, Q. Zhong, Q. Sun, J. Peng, H. Gong, and J. Yuan, “Simultaneous acquisition of neuronal morphology and cytoarchitecture in the same Golgi-stained brain,” Biomed. Opt. Express 9(1), 230–244 (2018).
[Crossref] [PubMed]

2017 (3)

2015 (2)

2013 (1)

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

2012 (1)

2010 (1)

D. S. C. Biggs, “3D deconvolution microscopy,” Current Protocols in Cytometry 52(1), 1–20 (2010).

2006 (2)

P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Process. Mag. 23(3), 32–45 (2006).
[Crossref]

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref] [PubMed]

2004 (2)

J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E. H. K. Stelzer, “Optical sectioning deep inside live embryos by selective plane illumination microscopy,” Science 305(5686), 1007–1009 (2004).
[Crossref] [PubMed]

L. H. Schaefer, D. Schuster, and J. Schaffer, “Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach,” J. Microsc. 216(2), 165–174 (2004).
[Crossref] [PubMed]

1997 (1)

1990 (1)

W. Denk, J. H. Strickler, and W. W. Webb, “Two-photon laser scanning fluorescence microscopy,” Science 248(4951), 73–76 (1990).
[Crossref] [PubMed]

1983 (1)

M. A. King, R. B. Schwinger, and P. W. Doherty, “Fast Wiener digital post-processing of SPECT images,” J. Nucl. Med. 24, 81–82 (1983).

1974 (1)

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79(6), 745–749 (1974).
[Crossref]

1972 (2)

W. H. Richardson, “Bayesian-based iterative method of image restoration,” JOSA 62(1), 55–59 (1972).
[Crossref]

A. Klug and R. A. Crowther, “Three-dimensional image reconstruction from the viewpoint of information theory,” Nature 238(5365), 435–440 (1972).
[Crossref]

Acosta, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

Aitken, A. P.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

Ando, D. M.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Aristov, A.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Barbastathis, G.

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Berndl, M.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Biggs, D. S. C.

D. S. C. Biggs, “3D deconvolution microscopy,” Current Protocols in Cytometry 52(1), 1–20 (2010).

Blanc-Feraud, L.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref] [PubMed]

Caballero, J.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

Chen, X.

Christiansen, E. M.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Crowther, R. A.

A. Klug and R. A. Crowther, “Three-dimensional image reconstruction from the viewpoint of information theory,” Nature 238(5365), 435–440 (1972).
[Crossref]

Cunningham, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

Del Bene, F.

J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E. H. K. Stelzer, “Optical sectioning deep inside live embryos by selective plane illumination microscopy,” Science 305(5686), 1007–1009 (2004).
[Crossref] [PubMed]

Denk, W.

W. Denk, J. H. Strickler, and W. W. Webb, “Two-photon laser scanning fluorescence microscopy,” Science 248(4951), 73–76 (1990).
[Crossref] [PubMed]

Dey, N.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref] [PubMed]

Doherty, P. W.

M. A. King, R. B. Schwinger, and P. W. Doherty, “Fast Wiener digital post-processing of SPECT images,” J. Nucl. Med. 24, 81–82 (1983).

Esteva, A.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Fedus, W.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Feng, Z.

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

Finkbeiner, S.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Frosio, I.

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imaging 3(1), 47–57 (2017).
[Crossref]

Gallo, O.

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imaging 3(1), 47–57 (2017).
[Crossref]

Gao, L.

Gong, H.

X. Chen, X. Zhang, Q. Zhong, Q. Sun, J. Peng, H. Gong, and J. Yuan, “Simultaneous acquisition of neuronal morphology and cytoarchitecture in the same Golgi-stained brain,” Biomed. Opt. Express 9(1), 230–244 (2018).
[Crossref] [PubMed]

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

Göröcs, Z.

Goy, A.

Goyal, P.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Günaydin, H.

Gustafsson, M. G. L.

E. A. Ingerman, R. A. London, R. Heintzmann, and M. G. L. Gustafsson, “Signal, noise and resolution in linear and nonlinear structured-illumination microscopy,” J. Microsc.1 (2018). https://doi.org/10.1111/jmi.12753 .

Hagen, N.

Hao, X.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision, 2015), 1026–1034.
[Crossref]

Heintzmann, R.

E. A. Ingerman, R. A. London, R. Heintzmann, and M. G. L. Gustafsson, “Signal, noise and resolution in linear and nonlinear structured-illumination microscopy,” J. Microsc.1 (2018). https://doi.org/10.1111/jmi.12753 .

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Hinton, G. E.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), 2010), pp. 807–814.

Hu, B.

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

Huisken, J.

J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E. H. K. Stelzer, “Optical sectioning deep inside live embryos by selective plane illumination microscopy,” Science 305(5686), 1007–1009 (2004).
[Crossref] [PubMed]

Huszár, F.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

Ingerman, E. A.

E. A. Ingerman, R. A. London, R. Heintzmann, and M. G. L. Gustafsson, “Signal, noise and resolution in linear and nonlinear structured-illumination microscopy,” J. Microsc.1 (2018). https://doi.org/10.1111/jmi.12753 .

Javaherian, A.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Jiang, T.

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

Juškaitis, R.

Kam, Z.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref] [PubMed]

Kamilov, U. S.

Kautz, J.

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imaging 3(1), 47–57 (2017).
[Crossref]

King, M. A.

M. A. King, R. B. Schwinger, and P. W. Doherty, “Fast Wiener digital post-processing of SPECT images,” J. Nucl. Med. 24, 81–82 (1983).

Klug, A.

A. Klug and R. A. Crowther, “Three-dimensional image reconstruction from the viewpoint of information theory,” Nature 238(5365), 435–440 (1972).
[Crossref]

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Ledig, C.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

Lee, A. K.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

Lee, J.

Lelek, M.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Li, A.

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

Li, S.

Li, Y.

Lin, X.

Lipnick, S.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
[Crossref] [PubMed]

London, R. A.

E. A. Ingerman, R. A. London, R. Heintzmann, and M. G. L. Gustafsson, “Signal, noise and resolution in linear and nonlinear structured-illumination microscopy,” J. Microsc.1 (2018). https://doi.org/10.1111/jmi.12753 .

Lucy, L. B.

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79(6), 745–749 (1974).
[Crossref]

Luo, Q.

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
[Crossref] [PubMed]

Michaeli, T.

Mount, E.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
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Nair, V.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), 2010), pp. 807–814.

Nehme, E.

Nehmetallah, G.

Nehorai, A.

P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Process. Mag. 23(3), 32–45 (2006).
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Nelson, P.

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O’Neil, A.

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
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N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
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E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
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L. H. Schaefer, D. Schuster, and J. Schaffer, “Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach,” J. Microsc. 216(2), 165–174 (2004).
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E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
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E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
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Swoger, J.

J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E. H. K. Stelzer, “Optical sectioning deep inside live embryos by selective plane illumination microscopy,” Science 305(5686), 1007–1009 (2004).
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Totz, J.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, and Z. Wang, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR, 1–19 (2017).

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J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E. H. K. Stelzer, “Optical sectioning deep inside live embryos by selective plane illumination microscopy,” Science 305(5686), 1007–1009 (2004).
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Zeng, S.

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
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N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
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H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imaging 3(1), 47–57 (2017).
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Zhong, Q.

Zimmer, C.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
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N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
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Astron. J. (1)

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79(6), 745–749 (1974).
[Crossref]

Biomed. Opt. Express (1)

Cell (1)

E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, “In silico labeling: Predicting fluorescent labels in unlabeled images,” Cell 173(3), 792–803 (2018).
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IEEE Signal Process. Mag. (1)

P. Sarder and A. Nehorai, “Deconvolution methods for 3-D fluorescence microscopy images,” IEEE Signal Process. Mag. 23(3), 32–45 (2006).
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IEEE Trans. Comput. Imaging (1)

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imaging 3(1), 47–57 (2017).
[Crossref]

J. Biomed. Opt. (1)

D. Xu, T. Jiang, A. Li, B. Hu, Z. Feng, H. Gong, S. Zeng, and Q. Luo, “Fast optical sectioning obtained by structured illumination microscopy using a digital mirror device,” J. Biomed. Opt. 18(6), 60503 (2013).
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J. Microsc. (1)

L. H. Schaefer, D. Schuster, and J. Schaffer, “Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach,” J. Microsc. 216(2), 165–174 (2004).
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J. Nucl. Med. (1)

M. A. King, R. B. Schwinger, and P. W. Doherty, “Fast Wiener digital post-processing of SPECT images,” J. Nucl. Med. 24, 81–82 (1983).

JOSA (1)

W. H. Richardson, “Bayesian-based iterative method of image restoration,” JOSA 62(1), 55–59 (1972).
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Light Sci. Appl. (1)

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7(2), 17141 (2018).
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Microsc. Res. Tech. (1)

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Nat. Biotechnol. (1)

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
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Opt. Express (2)

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Optica (5)

Science (2)

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J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila, “Noise2Noise: Learning image restoration without clean data,” arXiv preprint arXiv:1803.04189 (2018).

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Figures (5)

Fig. 1
Fig. 1 Overview of the operation of our optical sectioning method. (a) Schematic of the convolutional neural network. (b) The two main stages of operation with our technique: ① training the network using a wide-field (WF) image and a corresponding optically-sectioned reference image, and ② reconstructing the new WF image using the trained network.
Fig. 2
Fig. 2 A comparison of images of a tdTomato-labeled heart tissue sample reconstructed using, (a) wide-field overlapping, (b) SIM algorithm (c) RL deconvolution, and (d) CNN model prediction techniques. (e) Normalized intensity profiles of the regions defined by the white dashed lines in the insets in (a–d). The white arrow in the inset of 2(b) labels the stripe artifacts generated in SIM reconstruction. Scale bar: 100 μm (10 μm in the inset).
Fig. 3
Fig. 3 Images of (a) PI-counterstained cytoarchitecture, (b) Thy1-YFP transgenic mouse brain tissue, and (c) Golgi-staining brain tissue obtained using different imaging and reconstructing methods. (d–f) Normalized intensity profiles of the regions defined by the white dashed lines in (a–c), respectively. Scale bar: 10 μm.
Fig. 4
Fig. 4 Continuous image acquisition of a 60 μm-thick GMA resin-embedded Thy1-GFP transgenic mouse brain tissue sample, at a z step of 1 μm. 3D reconstruction and maximum intensity projections of the XY and XZ planes obtained using (a) WF imaging, (b) SIM imaging and (c) our CNN method. The blue arrows highlight somas and fibers, illustrating that our method retains image depth better than SIM. Scale bar: 50 μm.
Fig. 5
Fig. 5 A comparison of optically-sectioned images predicted using well-trained models from data sets recorded by microscopes differing from the one used in experimentation. (a) Test images recorded using a Nikon wide-field microscope with 4 × , 10 × , and 20 × objectives. (b) Images predicted by a CNN model trained using an image pair acquired by another Nikon confocal microscope with pinholes size of 11.4 AU and 1 AU. (c) Images predicted by the CNN model used for Fig. 4. (d) Confocal images produced by the microscope used to train the CNN model applied in (b). Scale bars from top to bottom: 500, 200, and 100 μm.

Tables (2)

Tables Icon

Table 1 Quantitative noise analysis

Tables Icon

Table 2 Comparison of processing time with different a1.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

h(v)= P(ρ) J 0 (vρ)ρdρ
l= a 2 a 1 k1
L= 1 B b=1 B { 1 M*N } m=1 M n=1 N ( Y ^ m,n Y m,n ) 2