Abstract

Digital holographic microscopy (DHM) as a label-free quantitative imaging tool has been widely used to investigate the morphology of living cells dynamically. In the off-axis DHM, the spatial filtering in the frequency spectrum of the hologram is vital to the quality of the reconstructed images. In this paper, we propose an adaptive spatial filtering approach based on convolutional neural networks (CNN) to automatically extracts the optimal shape of frequency components. For achieving robust and precise recognition performance, the net model is trained by using the tens of thousands of frequency spectrums with a variety of specimens and imaging conditions. The experimental results demonstrate that the trained network produce an adaptive spatial filtering window which can accurately select the frequency components of the object term and eliminate the frequency components of the interference terms, especially the coherent noise that overlaps with the object term in the spatial frequency domain. We find that the proposed approach has a fast, robust, and outstanding frequency filtering capability without any manual intervention and initial input parameters compared to previous techniques. Furthermore, the applicability of the proposed method in off-axis DHM for dynamic analysis is demonstrated by real-time monitoring the morphologic changes of living MLO-Y4 cells that are constantly subject to Fluid Shear Stress (FSS).

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2017 (4)

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus classification in digital holographic microscopy using deep convolutional neural networks,” Proc. SPIE 10414, 104140K (2017).
[Crossref]

T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25(13), 15043–15057 (2017).
[Crossref] [PubMed]

R. Cao, W. Xiao, X. Wu, L. Sun, and F. Pan, “Quantitative observations on cytoskeleton changes of osteocytes at different cell parts using digital holographic microscopy,” Biomed. Opt. Express 9(1), 72–85 (2017).
[Crossref] [PubMed]

2016 (1)

2015 (1)

2014 (4)

J. Weng, H. Li, Z. Zhang, and J. Zhong, “Design of adaptive spatial filter at uniform standard for automatic analysis of digital holographic microscopy,” Optik (Stuttg.) 125(11), 2633–2637 (2014).
[Crossref]

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations & Trends in Signal Processing 7(3), 197–387 (2014).
[Crossref]

2013 (1)

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML 3(28), 1139–1147 (2013).

2011 (1)

O. J. Rincon, R. Amezquita, Y. M. Torres, and V. Agudelo, “Novel method for automatic filtering in the Fourier space applied to digital hologram reconstruction,” Proc. SPIE 8082, 80822E (2011).
[Crossref]

2010 (1)

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

2009 (1)

S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, and KLEIN, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration,” Int. J. Comput. Vis. 81(3), 227–239 (2009).
[Crossref]

2008 (1)

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

2006 (2)

2005 (3)

2000 (2)

1999 (2)

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Agudelo, V.

O. J. Rincon, R. Amezquita, Y. M. Torres, and V. Agudelo, “Novel method for automatic filtering in the Fourier space applied to digital hologram reconstruction,” Proc. SPIE 8082, 80822E (2011).
[Crossref]

Amezquita, R.

O. J. Rincon, R. Amezquita, Y. M. Torres, and V. Agudelo, “Novel method for automatic filtering in the Fourier space applied to digital hologram reconstruction,” Proc. SPIE 8082, 80822E (2011).
[Crossref]

Aspert, N.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Bevilacqua, F.

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 International Publishing, 2015).
[Crossref]

Bui, V.

Cao, R.

Chang, L. C.

Chang, L.-C.

V. Bui and L.-C. Chang, “Deep learning architectures for hard character classification,” in Proc. Int. Conf. Art if. Int. (2016), pp. 108.

Charrière, F.

Chen, J.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Chen, K.

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Chen, Z.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Choi, W.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Colomb, T.

Cross, M.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

Cuche, E.

Dahl, G. E.

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML 3(28), 1139–1147 (2013).

Danesh Panah, M.

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

Darrell, T.

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

Davis, A.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Dean, J.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Deng, L.

L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations & Trends in Signal Processing 7(3), 197–387 (2014).
[Crossref]

Depeursinge, C.

T. Colomb, J. Kühn, F. Charrière, C. Depeursinge, P. Marquet, and N. Aspert, “Total aberrations compensation in digital holographic microscopy with a reference conjugated hologram,” Opt. Express 14(10), 4300–4306 (2006).
[Crossref] [PubMed]

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref] [PubMed]

B. Rappaz, P. Marquet, E. Cuche, Y. Emery, C. Depeursinge, and P. Magistretti, “Measurement of the integral refractive index and dynamic cell morphometry of living cells with digital holographic microscopy,” Opt. Express 13(23), 9361–9373 (2005).
[Crossref] [PubMed]

E. Cuche, P. Marquet, and C. Depeursinge, “Spatial filtering for zero-order and twin-image elimination in digital off-axis holography,” Appl. Opt. 39(23), 4070–4075 (2000).
[Crossref] [PubMed]

E. Cuche, P. Marquet, and C. Depeursinge, “Spatial filtering for zero-order and twin-image elimination in digital off-axis holography,” Appl. Opt. 39(23), 4070–4075 (2000).
[Crossref] [PubMed]

E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38(34), 6994–7001 (1999).
[Crossref] [PubMed]

E. Cuche, F. Bevilacqua, and C. Depeursinge, “Digital holography for quantitative phase-contrast imaging,” Opt. Lett. 24(5), 291–293 (1999).
[Crossref] [PubMed]

Devin, 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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Diez-Silva, M.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Emery, Y.

Feld, M. S.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Ferraro, P.

Fischer, P.

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 International Publishing, 2015).
[Crossref]

Gao, J.

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

Ghemawat, S.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Han, B.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision (2015).

Hannun, A. Y.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. of the 30th Intern. Conf. on Machine Learning (ICML, 2013).

Haynie, D. T.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

He, X.

Hinton, G. E.

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML 3(28), 1139–1147 (2013).

Hong, J.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

Hong, S.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision (2015).

Hong, Y.

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Huang, J.

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

Irving, G.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Isard, 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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Javidi, B.

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

Kim, M.

Kim, M. K.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt.A 8(7), S518–S523 (2006).

Klein, S.

S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, and KLEIN, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration,” Int. J. Comput. Vis. 81(3), 227–239 (2009).
[Crossref]

Kudlur, 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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Kühn, J.

Lam, V.

Lee, W. M.

Levenberg, J.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Li, H.

J. Weng, H. Li, Z. Zhang, and J. Zhong, “Design of adaptive spatial filter at uniform standard for automatic analysis of digital holographic microscopy,” Optik (Stuttg.) 125(11), 2633–2637 (2014).
[Crossref]

Li, J.

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

Liao, G.

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Liu, C.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

Liu, Y.

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

Lo, C.-M.

Long, J.

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

Lykotrafitis, G.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Maas, A. L.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. of the 30th Intern. Conf. on Machine Learning (ICML, 2013).

Magistretti, P.

Magistretti, P. J.

Maier, A. G.

Mann, C.

Mann, C. J.

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt.A 8(7), S518–S523 (2006).

Manninen, A.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus classification in digital holographic microscopy using deep convolutional neural networks,” Proc. SPIE 10414, 104140K (2017).
[Crossref]

Marquet, P.

Martens, J.

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML 3(28), 1139–1147 (2013).

Matrecano, M.

Memmolo, P.

Miccio, L.

Monga, R.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Moore, S.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Murray, D. G.

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Naughton, T. J.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus classification in digital holographic microscopy using deep convolutional neural networks,” Proc. SPIE 10414, 104140K (2017).
[Crossref]

Nehmetallah, G.

Ng, A. Y.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. of the 30th Intern. Conf. on Machine Learning (ICML, 2013).

Nguyen, C. V.

Nguyen, T.

Nisbet, D. R.

Noh, H.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision (2015).

Osten, W.

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

Pan, F.

Park, Y.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Persano, A.

Pitkäaho, T.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus classification in digital holographic microscopy using deep convolutional neural networks,” Proc. SPIE 10414, 104140K (2017).
[Crossref]

Pluim, J. P. W.

S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, and KLEIN, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration,” Int. J. Comput. Vis. 81(3), 227–239 (2009).
[Crossref]

Popescu, G.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Pratap, M.

Quaranta, F.

Rappaz, B.

Raub, C. B.

Rincon, O. J.

O. J. Rincon, R. Amezquita, Y. M. Torres, and V. Agudelo, “Novel method for automatic filtering in the Fourier space applied to digital hologram reconstruction,” Proc. SPIE 8082, 80822E (2011).
[Crossref]

Ronneberger, O.

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 International Publishing, 2015).
[Crossref]

Rug, M.

Schaal, F.

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

Shelhamer, E.

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

Shi, T.

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Siciliano, P.

Staring, M.

S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, and KLEIN, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration,” Int. J. Comput. Vis. 81(3), 227–239 (2009).
[Crossref]

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Sun, L.

Suresh, S.

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Sutskever, I.

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML 3(28), 1139–1147 (2013).

Torres, Y. M.

O. J. Rincon, R. Amezquita, Y. M. Torres, and V. Agudelo, “Novel method for automatic filtering in the Fourier space applied to digital hologram reconstruction,” Proc. SPIE 8082, 80822E (2011).
[Crossref]

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Viergever, M. A.

S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, and KLEIN, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration,” Int. J. Comput. Vis. 81(3), 227–239 (2009).
[Crossref]

Wang, X.

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Wang, Y.

Wang, Z.

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

Warber, M.

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Weng, J.

J. Weng, H. Li, Z. Zhang, and J. Zhong, “Design of adaptive spatial filter at uniform standard for automatic analysis of digital holographic microscopy,” Optik (Stuttg.) 125(11), 2633–2637 (2014).
[Crossref]

Wicke, 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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Williams, R. J.

Wu, X.

Xiao, W.

Yu, D.

L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations & Trends in Signal Processing 7(3), 197–387 (2014).
[Crossref]

Yu, L.

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt.A 8(7), S518–S523 (2006).

C. Mann, L. Yu, C.-M. Lo, and M. Kim, “High-resolution quantitative phase-contrast microscopy by digital holography,” Opt. Express 13(22), 8693–8698 (2005).
[Crossref] [PubMed]

Yu, X.

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Zhang, Y.

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Zhang, Z.

J. Weng, H. Li, Z. Zhang, and J. Zhong, “Design of adaptive spatial filter at uniform standard for automatic analysis of digital holographic microscopy,” Optik (Stuttg.) 125(11), 2633–2637 (2014).
[Crossref]

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

Zheng, Y.

Zhong, J.

J. Weng, H. Li, Z. Zhang, and J. Zhong, “Design of adaptive spatial filter at uniform standard for automatic analysis of digital holographic microscopy,” Optik (Stuttg.) 125(11), 2633–2637 (2014).
[Crossref]

Zwick, S.

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

Appl. Opt. (4)

Biomed. Opt. Express (2)

Display Technology, Journalism (1)

M. Danesh Panah, S. Zwick, F. Schaal, M. Warber, B. Javidi, and W. Osten, “3D holographic imaging and trapping for non-invasive cell identification and tracking,” Display Technology, Journalism 6(10), 490–499 (2010).
[Crossref]

Foundations & Trends in Signal Processing (1)

L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations & Trends in Signal Processing 7(3), 197–387 (2014).
[Crossref]

ICML (1)

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton, “On the importance of initialization and momentum in deep learning,” ICML 3(28), 1139–1147 (2013).

Int. J. Comput. Vis. (1)

S. Klein, J. P. W. Pluim, M. Staring, M. A. Viergever, and KLEIN, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration,” Int. J. Comput. Vis. 81(3), 227–239 (2009).
[Crossref]

J. Biomed. Opt. (1)

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

J. Opt.A (1)

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt.A 8(7), S518–S523 (2006).

Opt. Commun. (1)

Y. Hong, T. Shi, X. Wang, Y. Zhang, K. Chen, and G. Liao, “Weighted adaptive spatial filtering in digital holographic microscopy,” Opt. Commun. 382, 624–631 (2017).
[Crossref]

Opt. Eng. (1)

J. Li, Z. Wang, J. Gao, Y. Liu, and J. Huang, “Adaptive spatial filtering based on region growing for automatic analysis in digital holographic microscopy,” Opt. Eng. 54(3), 031103 (2014).
[Crossref]

Opt. Express (4)

Opt. Lett. (2)

Optik (Stuttg.) (1)

J. Weng, H. Li, Z. Zhang, and J. Zhong, “Design of adaptive spatial filter at uniform standard for automatic analysis of digital holographic microscopy,” Optik (Stuttg.) 125(11), 2633–2637 (2014).
[Crossref]

Proc. Natl. Acad. Sci. U.S.A. (1)

Y. Park, M. Diez-Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, “Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 13730–13735 (2008).
[Crossref] [PubMed]

Proc. SPIE (2)

O. J. Rincon, R. Amezquita, Y. M. Torres, and V. Agudelo, “Novel method for automatic filtering in the Fourier space applied to digital hologram reconstruction,” Proc. SPIE 8082, 80822E (2011).
[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus classification in digital holographic microscopy using deep convolutional neural networks,” Proc. SPIE 10414, 104140K (2017).
[Crossref]

Other (11)

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, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016).

T. Kreis, Digital Recording and Numerical Reconstruction of Wave Fields (Academic, 2005).

K. Myung, Kim, Digital Holographic Microscopy (Academic, 2011).

G. Nehmetallah, R. Aylo, and L. Williams, Analog and Digital Holography with MATLAB®, (SPIE Press, 2015).

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 International Publishing, 2015).
[Crossref]

V. Bui and L.-C. Chang, “Deep learning architectures for hard character classification,” in Proc. Int. Conf. Art if. Int. (2016), pp. 108.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. of the 30th Intern. Conf. on Machine Learning (ICML, 2013).

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” [arXiv preprint arXiv:1502.03167

D. C. Ciresan, L. M. Gambardella, A. Giusti, and J. Schmidhuber, “Deep neural networks segment neuronal membranes in electron microscopy images.” in NIPS. (2012), pp. 2852–2860.

H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision (2015).

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

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Figures (11)

Fig. 1
Fig. 1 Schematic setup of DHM system (NDF: neutral density filter; 1/2λ: half-wave plate; M1-M3: mirrors; SF: spatial filter; BS: beam splitter; PBS: polarizing beam splitter; CL: condenser lens; TL: tube lens.)
Fig. 2
Fig. 2 The reconstructed images with two different filter windows. (a) digital hologram. (b) the frequency spectrum of the hologram, the red window determined by manual and blue window determined by a typical threshold method. (c) and (d) are reconstructed phase images that correspond to the manual filter window and the threshold window respectively. (e) and (f) are the zoom-in images in (c) and (d).
Fig. 3
Fig. 3 The proposed filter method based no CNN model. (a) the hologram. (b) the frequency spectrum of the hologram. (c) the half of the frequency spectrum after cropping. (d) the spatial filter window came from the trained CNN. (e) the filtered frequency spectrums. (f) The centered frequency spectrum. (g) the reconstructed phase image.
Fig. 4
Fig. 4 U-net Convolutional Neural Network model architecture. Each green box corresponds to a multi-channel feature map. The number of channels is on top of the box. The x-y-size is provided at the edge of the box. Gray boxes represent copied feature maps. The arrows denote the different operations.
Fig. 5
Fig. 5 Training loss and accuracy of the CNN models for 15000 steps.
Fig. 6
Fig. 6 Visualization of the output of a selected channel from layers as followings: 4, 7, 10, 13, 18, 21, 24, 27 and 28 in CNN model.
Fig. 7
Fig. 7 The results of the proposed method for the different types of cells. The first row is the results about endometrial carcinoma cell with (a) the frequency spectrum of the hologram, (b) the filter window by CNN model, and (c) the reconstructed phase images. The second row is the results about osteocyte MLO-Y4 cells with (d) the frequency spectrum, (e) the filter windows, and (f) the reconstruction. The last row is the results about ovarian cancer cells with (g) the frequency spectrum, (h) the filter windows, and (i) the reconstruction.
Fig. 8
Fig. 8 The comparison of the proposed method with the existing four methods. (a) the digital hologram. (b) the zoom-in images of the hologram. (c) the frequency spectrum. (d) the zoom-in images of the spectrum. (e) the filtered spectrum by the Histogram-analysis Filter (FHA). (f) the filtered spectrum by the Butterworth filter (FBW). (g) the filtered spectrum by the Region-recognition Filter (FRR). (h) the filtered spectrum by the Manual-selection Filter (FMS). (i) the filtered spectrum by the Deep-learning Filter (FDL). (j), (k), (l), (m), and (n) are the corresponding phase images reconstructed from (e), (f), (g), (h), and (i). (o), (p), (q), (r), and (s) are the zoom-in images in (j), (k), (l), (m), and (n), respectively.
Fig. 9
Fig. 9 Schematic setup of the fluid shear system
Fig. 10
Fig. 10 Monitoring result of living MLO-Y4 cells under FSS with the proposed method.
Fig. 11
Fig. 11 Phase images and the corresponding profiles of the red line at the beginning and end of the experiments (red line represent initial time while blue line represent the end)

Equations (5)

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

I = | O | 2 + | R | 2 + O R * + O * R
A = F T { | O 2 | + | R 2 | }+ F T { O R * }+ F T { O * R }= A 1 + A 2 + A 3
A 2 = F T { O R * } = H × A = H × F T { I }
A 2 ( k x , k y ; z ) = A 2 ( k x , k y ; 0 ) exp ( j z k 2 k x 2 k y 2 )
U ( x , y ; z ) = F T 1 { A 2 ( k x , k y ; z ) }

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