Abstract

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells’ motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

© 2017 Optical Society of America

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2016 (3)

2015 (2)

2014 (3)

L. Williams, P. P. Banerjee, G. Nehmetallah, and S. Praharaj, “Holographic volume displacement calculations via multiwavelength digital holography,” Appl. Opt. 53(8), 1597–1603 (2014).
[Crossref] [PubMed]

Y. Fang, C. Y. Y. Iu, C. N. P. Lui, Y. Zou, C. K. M. Fung, H. W. Li, N. Xi, K. K. L. Yung, and K. W. C. Lai, “Investigating dynamic structural and mechanical changes of neuroblastoma cells associated with glutamate-mediated neurodegeneration,” Sci. Rep. 4, 7074 (2014).
[PubMed]

K. Hien, T. Pan, Z. Wang, M. Le, H. Nguyen, and M. Vo, “Accurate 3D shape measurement of multiple separate objects with stereo vision,” Meas. Sci. Technol. 25(3), 035401 (2014).
[Crossref]

2013 (4)

W. Khan, “Image segmentation techniques: A survey,” J. Image Graphics 1(4), 166–170 (2013).

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).

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

C. Zuo, Q. Chen, W. Qu, and A. Asundi, “Phase aberration compensation in digital holographic microscopy based on principal component analysis,” Opt. Lett. 38(10), 1724–1726 (2013).
[Crossref] [PubMed]

2012 (3)

T. T. A. Nguyen, H. N. D. Le, M. Vo, Z. Wang, L. Luu, and J. C. Ramella-Roman, “Three-dimensional phantoms for curvature correction in spatial frequency domain imaging,” Biomed. Opt. Express 3(6), 1200–1214 (2012).
[Crossref] [PubMed]

N. Pavillon, J. Kühn, C. Moratal, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Early cell death detection with digital holographic microscopy,” PLoS One 7(1), e30912 (2012).
[Crossref] [PubMed]

G. Nehmetallah and P. P. Banerjee, “Applications of digital and analog holography in 3D imaging,” Adv. Opt. Photonics 4(4), 472–553 (2012).
[Crossref]

2010 (2)

N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
[Crossref] [PubMed]

N. Warnasooriya, F. Joud, P. Bun, G. Tessier, M. Coppey-Moisan, P. Desbiolles, M. Atlan, M. Abboud, and M. Gross, “Imaging gold nanoparticles in living cell environments using heterodyne digital holographic microscopy,” Opt. Express 18(4), 3264–3273 (2010).
[Crossref] [PubMed]

2009 (2)

J. Kühn, F. Montfort, T. Colomb, B. Rappaz, C. Moratal, N. Pavillon, P. Marquet, and C. Depeursinge, “Submicrometer tomography of cells by multiple-wavelength digital holographic microscopy in reflection,” Opt. Lett. 34(5), 653–655 (2009).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

2007 (2)

X. Bresson, S. Esedoḡlu, P. Vandergheynst, J. P. Thiran, and S. Osher, “Fast global minimization of the active contour/snake model,” J. Math. Imaging Vis. 28(2), 151–167 (2007).
[Crossref]

C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
[Crossref] [PubMed]

2006 (4)

2002 (1)

2000 (1)

J. B. M. T. Roerdink and A. Meijster, “The watershed transform: Definitions, algorithms and parallelization strategies,” Fundam. Inform. 41(1–2), 187–228 (2000).

1999 (1)

1998 (1)

S. A. Hojjatoleslami and J. Kittler, “Region growing: a new approach,” IEEE Trans. Image Process. 7(7), 1079–1084 (1998).
[Crossref] [PubMed]

1988 (1)

R. H. Laprade, “Split-and-merge segmentation of aerial photographs,” Comput. Vis. Graph. Image Process. 44(1), 77–86 (1988).
[Crossref]

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).

Abboud, M.

Aspert, N.

Asundi, A.

Atlan, M.

Aylo, R.

Banerjee, P. P.

L. Williams, P. P. Banerjee, G. Nehmetallah, and S. Praharaj, “Holographic volume displacement calculations via multiwavelength digital holography,” Appl. Opt. 53(8), 1597–1603 (2014).
[Crossref] [PubMed]

G. Nehmetallah and P. P. Banerjee, “Applications of digital and analog holography in 3D imaging,” Adv. Opt. Photonics 4(4), 472–553 (2012).
[Crossref]

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).

Benke, A.

N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
[Crossref] [PubMed]

Boss, D.

N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
[Crossref] [PubMed]

Bourquin, S.

Boykov, Y.

Y. Boykov and F. L. Gareth, “Graph cuts and efficient ND image segmentation,” Int. J. Comput. Vis. 70(2), 109–131 (2006).
[Crossref]

Bresson, X.

X. Bresson, S. Esedoḡlu, P. Vandergheynst, J. P. Thiran, and S. Osher, “Fast global minimization of the active contour/snake model,” J. Math. Imaging Vis. 28(2), 151–167 (2007).
[Crossref]

Breton, B.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

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.

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

Bun, P.

Burton, D. R.

Cano, E.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

Chambon, M.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

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, Q.

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).

Colomb, T.

Coppey-Moisan, M.

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).

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).

Darudi, A.

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).

Depeursinge, C.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

N. Pavillon, J. Kühn, C. Moratal, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Early cell death detection with digital holographic microscopy,” PLoS One 7(1), e30912 (2012).
[Crossref] [PubMed]

N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

J. Kühn, F. Montfort, T. Colomb, B. Rappaz, C. Moratal, N. Pavillon, P. Marquet, and C. Depeursinge, “Submicrometer tomography of cells by multiple-wavelength digital holographic microscopy in reflection,” Opt. Lett. 34(5), 653–655 (2009).
[Crossref] [PubMed]

T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
[Crossref] [PubMed]

T. Colomb, F. Montfort, J. Kühn, N. Aspert, E. Cuche, A. Marian, F. Charrière, S. Bourquin, P. Marquet, and C. Depeursinge, “Numerical parametric lens for shifting, magnification, and complete aberration compensation in digital holographic microscopy,” J. Opt. Soc. Am. A 23(12), 3177–3190 (2006).
[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]

Desbiolles, P.

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).

Emery, Y.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

Esedo?lu, S.

X. Bresson, S. Esedoḡlu, P. Vandergheynst, J. P. Thiran, and S. Osher, “Fast global minimization of the active contour/snake model,” J. Math. Imaging Vis. 28(2), 151–167 (2007).
[Crossref]

Fang, Y.

Y. Fang, C. Y. Y. Iu, C. N. P. Lui, Y. Zou, C. K. M. Fung, H. W. Li, N. Xi, K. K. L. Yung, and K. W. C. Lai, “Investigating dynamic structural and mechanical changes of neuroblastoma cells associated with glutamate-mediated neurodegeneration,” Sci. Rep. 4, 7074 (2014).
[PubMed]

Fischer, P.

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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, 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).

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J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
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H. Nguyen, D. Nguyen, Z. Wang, H. Kieu, and M. Le, “Real-time, high-accuracy 3D imaging and shape measurement,” Appl. Opt. 54(1), A9–A17 (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, 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).

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Y. Fang, C. Y. Y. Iu, C. N. P. Lui, Y. Zou, C. K. M. Fung, H. W. Li, N. Xi, K. K. L. Yung, and K. W. C. Lai, “Investigating dynamic structural and mechanical changes of neuroblastoma cells associated with glutamate-mediated neurodegeneration,” Sci. Rep. 4, 7074 (2014).
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N. Pavillon, J. Kühn, C. Moratal, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Early cell death detection with digital holographic microscopy,” PLoS One 7(1), e30912 (2012).
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N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
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Marian, A.

Marquet, P.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
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N. Pavillon, J. Kühn, C. Moratal, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Early cell death detection with digital holographic microscopy,” PLoS One 7(1), e30912 (2012).
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N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
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B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
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J. Kühn, F. Montfort, T. Colomb, B. Rappaz, C. Moratal, N. Pavillon, P. Marquet, and C. Depeursinge, “Submicrometer tomography of cells by multiple-wavelength digital holographic microscopy in reflection,” Opt. Lett. 34(5), 653–655 (2009).
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T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
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T. Colomb, F. Montfort, J. Kühn, N. Aspert, E. Cuche, A. Marian, F. Charrière, S. Bourquin, P. Marquet, and C. Depeursinge, “Numerical parametric lens for shifting, magnification, and complete aberration compensation in digital holographic microscopy,” J. Opt. Soc. Am. A 23(12), 3177–3190 (2006).
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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).

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Meijster, A.

J. B. M. T. Roerdink and A. Meijster, “The watershed transform: Definitions, algorithms and parallelization strategies,” Fundam. Inform. 41(1–2), 187–228 (2000).

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J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
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C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
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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, 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).

Montfort, F.

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).

Moratal, C.

N. Pavillon, J. Kühn, C. Moratal, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Early cell death detection with digital holographic microscopy,” PLoS One 7(1), e30912 (2012).
[Crossref] [PubMed]

N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
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J. Kühn, F. Montfort, T. Colomb, B. Rappaz, C. Moratal, N. Pavillon, P. Marquet, and C. Depeursinge, “Submicrometer tomography of cells by multiple-wavelength digital holographic microscopy in reflection,” Opt. Lett. 34(5), 653–655 (2009).
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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).

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, D.

Nguyen, H.

H. Nguyen, D. Nguyen, Z. Wang, H. Kieu, and M. Le, “Real-time, high-accuracy 3D imaging and shape measurement,” Appl. Opt. 54(1), A9–A17 (2015).
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K. Hien, T. Pan, Z. Wang, M. Le, H. Nguyen, and M. Vo, “Accurate 3D shape measurement of multiple separate objects with stereo vision,” Meas. Sci. Technol. 25(3), 035401 (2014).
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Nguyen, T. T. A.

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

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K. Hien, T. Pan, Z. Wang, M. Le, H. Nguyen, and M. Vo, “Accurate 3D shape measurement of multiple separate objects with stereo vision,” Meas. Sci. Technol. 25(3), 035401 (2014).
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Pandiyan, V. P.

Parent, J.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
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Pavillon, N.

N. Pavillon, J. Kühn, C. Moratal, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Early cell death detection with digital holographic microscopy,” PLoS One 7(1), e30912 (2012).
[Crossref] [PubMed]

N. Pavillon, A. Benke, D. Boss, C. Moratal, J. Kühn, P. Jourdain, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Cell morphology and intracellular ionic homeostasis explored with a multimodal approach combining epifluorescence and digital holographic microscopy,” J. Biophotonics 3(7), 432–436 (2010).
[Crossref] [PubMed]

J. Kühn, F. Montfort, T. Colomb, B. Rappaz, C. Moratal, N. Pavillon, P. Marquet, and C. Depeursinge, “Submicrometer tomography of cells by multiple-wavelength digital holographic microscopy in reflection,” Opt. Lett. 34(5), 653–655 (2009).
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Praharaj, S.

Putnam, A. J.

C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
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Qu, W.

Ramella-Roman, J. C.

Rappaz, B.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
[Crossref] [PubMed]

J. Kühn, F. Montfort, T. Colomb, B. Rappaz, C. Moratal, N. Pavillon, P. Marquet, and C. Depeursinge, “Submicrometer tomography of cells by multiple-wavelength digital holographic microscopy in reflection,” Opt. Lett. 34(5), 653–655 (2009).
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Raub, C.

Raub, C. B.

C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
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Roerdink, J. B. M. T.

J. B. M. T. Roerdink and A. Meijster, “The watershed transform: Definitions, algorithms and parallelization strategies,” Fundam. Inform. 41(1–2), 187–228 (2000).

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).
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Shaffer, E.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
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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).

Simanis, V.

B. Rappaz, E. Cano, T. Colomb, J. Kühn, C. Depeursinge, V. Simanis, P. J. Magistretti, and P. Marquet, “Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy,” J. Biomed. Opt. 14(3), 034049 (2009).
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Soltani, P.

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).

Suresh, V.

C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
[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).

Tessier, G.

Thiran, J. P.

X. Bresson, S. Esedoḡlu, P. Vandergheynst, J. P. Thiran, and S. Osher, “Fast global minimization of the active contour/snake model,” J. Math. Imaging Vis. 28(2), 151–167 (2007).
[Crossref]

Tran, D.

Tromberg, B. J.

C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
[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, 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).

Turcatti, G.

J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013).
[Crossref] [PubMed]

Vandergheynst, P.

X. Bresson, S. Esedoḡlu, P. Vandergheynst, J. P. Thiran, and S. Osher, “Fast global minimization of the active contour/snake model,” J. Math. Imaging Vis. 28(2), 151–167 (2007).
[Crossref]

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).

Vo, M.

K. Hien, T. Pan, Z. Wang, M. Le, H. Nguyen, and M. Vo, “Accurate 3D shape measurement of multiple separate objects with stereo vision,” Meas. Sci. Technol. 25(3), 035401 (2014).
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Wang, Z.

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).

Warnasooriya, N.

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, L.

Xi, N.

Y. Fang, C. Y. Y. Iu, C. N. P. Lui, Y. Zou, C. K. M. Fung, H. W. Li, N. Xi, K. K. L. Yung, and K. W. C. Lai, “Investigating dynamic structural and mechanical changes of neuroblastoma cells associated with glutamate-mediated neurodegeneration,” Sci. Rep. 4, 7074 (2014).
[PubMed]

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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, 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).

Yung, K. K. L.

Y. Fang, C. Y. Y. Iu, C. N. P. Lui, Y. Zou, C. K. M. Fung, H. W. Li, N. Xi, K. K. L. Yung, and K. W. C. Lai, “Investigating dynamic structural and mechanical changes of neuroblastoma cells associated with glutamate-mediated neurodegeneration,” Sci. Rep. 4, 7074 (2014).
[PubMed]

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).

Zou, Y.

Y. Fang, C. Y. Y. Iu, C. N. P. Lui, Y. Zou, C. K. M. Fung, H. W. Li, N. Xi, K. K. L. Yung, and K. W. C. Lai, “Investigating dynamic structural and mechanical changes of neuroblastoma cells associated with glutamate-mediated neurodegeneration,” Sci. Rep. 4, 7074 (2014).
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Zuo, C.

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C. B. Raub, V. Suresh, T. Krasieva, J. Lyubovitsky, J. D. Mih, A. J. Putnam, B. J. Tromberg, and S. C. George, “Noninvasive assessment of collagen gel microstructure and mechanics using multiphoton microscopy,” Biophys. J. 92(6), 2212–2222 (2007).
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Figures (11)

Fig. 1
Fig. 1

The BT-DHM system in transmission mode: (a) vertical tabletop setup and (b) optical schematic.

Fig. 2
Fig. 2

Image reconstruction pipeline with phase aberration compensation based on CNN + ZPF: (a, b, c) Hologram, Fourier Spectrum of the hologram, and cropped + 1 order spectrum, respectively. (d, e) Wrapped phase and unwrapped phase of (c) respectively. (f) CNN trained model, (g) output binary segmentation, (h) visualization of background detection, (i) Zernike polynomial fitting, (j) phase aberration calculated from ZPF, (k) Fourier Spectrum after phase aberration compensation, (l) Fourier Spectrum with zero padding and centering, (m) reconstructed phase map using angular spectrum, and (n) final unwrapped phase map.

Fig. 3
Fig. 3

The pipeline of the data preparation process to be used to train the CNN model. The blue path collects data for single cell segmentations and binary masks. The red path collects sub-sampled phase aberration. The green path shows how the data is fed to the CNN model.

Fig. 4
Fig. 4

U-net Convolutional Neural Network model.

Fig. 5
Fig. 5

Training loss and validation loss for the 360 epochs.

Fig. 6
Fig. 6

Visualization of outputs of a selected channel from the following layers: 3, 6, 9, 12, 15, 18, 21, 24, 27, and 28 in CNN.

Fig. 7
Fig. 7

(a) Typical manual segmentation on the test image of Fig. 6, (b) CNN model’s segmentation, and (c) background (BG) dice coefficient and cell dice coefficient on 9 cases of the test data.

Fig. 8
Fig. 8

(a) Wrapped phase with aberration [256x256], (b) background detection after CNN [256x256], (c) CNN’s binary mask where background (colored portion) is fed into ZPF [256x256], (d) residual phase [256x256], (e) phase map after phase compensation [1024x1024], and (f) phase unwrapping [1024x1024].

Fig. 9
Fig. 9

(a) Phase compensation with PCA, (b) conjugated residual phase of (a), (c) phase compensation using CNN + ZPF, (d) conjugated residual phase of (c), (e) Zernike coefficients of phase difference between CNN + ZPF technique and PCA technique using 1/|log|ak|| scale, and (f) profiles of yellow dash lines in (a) and (c) corresponding to blue and red line, respectively. Yellow bars denote the flatness of region of interest.

Fig. 10
Fig. 10

(a) Phase aberration, (b) unwrapped phase overlaid with CNN’s image segmentation mask, where background (color denoted) is fed into ZPF, (c) conjugated residual phase using CNN + ZPF, (d) fibers are visible after aberration compensation and are indicated by blue arrows, and (e) phase profile along the dash line in (d). Yellow bars denote the flatness of region of interest.

Fig. 11
Fig. 11

(a) Phase aberration, (b) unwrapped phase overlaid with CNN’s image segmentation mask, where background (color denoted) is fed into ZPF, (c) conjugated residual phase using CNN + ZPF, (d) 3D phase after compensation, and (e) phase profile along the dashed diagonal line from left corner to right corner.

Equations (16)

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

H( f x ,  f y  )=F[ h( x,y ) ]= h( x,y )exp{ 2πi( x f x +y f y ) }dxdy
U( f x , f y )=H( f x , f y )exp( 2πi f z d ) 
u( ξ,η )= F 1 [ U( f x , f y ) ]= U( f x , f y )exp{ 2πi( ξ f x +η f y ) }d f x d f y
Δ ξ mag =λd/( NΔxM ),  Δ η mag = λd/( NΔyM ),  
T( ξ,η )= λ 2π φ ob ( ξ,η ) Δn
x ' l ( i ) = j=0 M W l ( j ) x ' l1 ( j ) + B l ( j ) ,i=1,2,,N,
f( x ' ( i ) )={   x ' ( i )           ,  if  x '( i ) >0 0              ,  otherwise  , i=1, 2,, N,
x' ^ ( i ) =γ x ( i ) μ[ x ( i ) ] σ 2 +ε +β,
S( y (i) )= e y (i) i=1 N e y (i) ,i=1,2...,N,
Є =  1 N i=1 N L ( i ) log(S( y (i) )),
B ( i ) ={       1,        if  y ( i ) 0.5      0,        otherwise  ,  i=1, 2,, N,
S( x,y )= i=0 5 j=0 5 p ij x i y j ,i+j5,
A= z i,j,p 1 P .
[ a 0 a 1   a 10 a 20 ]= [ z 0,0,0 z 1,0,0 z 4,0,0 z 0,5,0 z 0,0,1 z 1,0,1 z 4,0,1 z 0,5,1         z 0,0,10 z 1,0,10 z 4,0,10 z 0,5,10         z 0,0,p1 z 1,0,p1 z 4,0,p1 z 0,5,p1 z 0,0,p z 1,0,p z 4,0,p z 0,5,p ] 1 p=20   ×   [ p 00 p 10   p 40 p 05 ],
P conjugated =exp( j k=0 20 a k Z k ),  k=1, 2, , 21,
DC= 2| AA' | | A |+| A' | ,

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