Abstract

We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.

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

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2019 (2)

Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park, “Quantitative Phase Imaging and Artificial Intelligence: A Review,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–14 (2019).
[Crossref]

G. Kim, Y. Jo, H. Cho, H. S. Min, and Y. Park, “Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,” Biosens. Bioelectron. 123, 69–76 (2019).
[Crossref] [PubMed]

2018 (11)

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] [PubMed]

C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nat. Methods 15(11), 917–920 (2018).
[Crossref] [PubMed]

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]

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7(1), 69 (2018).
[Crossref] [PubMed]

Y. Li, Y. Xue, and L. Tian, “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5(10), 1181–1190 (2018).
[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5(7), 803–813 (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. Jeon, W. Jeong, K. Son, and H. Yang, “Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks,” Opt. Lett. 43(17), 4240–4243 (2018).
[Crossref] [PubMed]

G. Kim, S. Lee, S. Shin, and Y. Park, “Three-dimensional label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography,” Sci. Rep. 8(1), 1782 (2018).
[Crossref] [PubMed]

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12(10), 578–589 (2018).
[Crossref]

J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
[Crossref] [PubMed]

2017 (9)

S. A. Yang, J. Yoon, K. Kim, and Y. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease,” Cytometry A 91(5), 510–518 (2017).
[Crossref] [PubMed]

S. Shin, K. Kim, K. Lee, S. Lee, and Y. Park, “Effects of spatiotemporal coherence on interferometric microscopy,” Opt. Express 25(7), 8085–8097 (2017).
[Crossref] [PubMed]

H. Farrokhi, J. Boonruangkan, B. J. Chun, T. M. Rohith, A. Mishra, H. T. Toh, H. S. Yoon, and Y.-J. Kim, “Speckle reduction in quantitative phase imaging by generating spatially incoherent laser field at electroactive optical diffusers,” Opt. Express 25(10), 10791–10800 (2017).
[Crossref] [PubMed]

I. Choi, K. Lee, and Y. Park, “Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration,” Opt. Express 25(24), 30771–30779 (2017).
[Crossref] [PubMed]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

S. Rawat, S. Komatsu, A. Markman, A. Anand, and B. Javidi, “Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification,” Appl. Opt. 56(9), D127–D133 (2017).
[Crossref] [PubMed]

T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
[Crossref] [PubMed]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” Proc. IEEE Int. Conf. Comput. Vis. 2018, 2242–2251 (2017).
[Crossref]

2016 (5)

V. Bianco, P. Memmolo, M. Paturzo, A. Finizio, B. Javidi, and P. Ferraro, “Quasi noise-free digital holography,” Light Sci. Appl. 5(9), e16142 (2016).
[Crossref] [PubMed]

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
[Crossref]

M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
[Crossref] [PubMed]

J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
[Crossref] [PubMed]

K. Kim, K. Choe, I. Park, P. Kim, and Y. Park, “Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice,” Sci. Rep. 6(1), 33084 (2016).
[Crossref] [PubMed]

2015 (6)

2014 (1)

Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
[Crossref] [PubMed]

2012 (1)

2009 (2)

2007 (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

2006 (2)

X. Li, T. Yamauchi, H. Iwai, Y. Yamashita, H. Zhang, and T. Hiruma, “Full-field quantitative phase imaging by white-light interferometry with active phase stabilization and its application to biological samples,” Opt. Lett. 31(12), 1830–1832 (2006).
[Crossref] [PubMed]

F. Dubois, C. Yourassowsky, O. Monnom, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

2004 (4)

S. Sotthivirat and J. A. Fessler, “Penalized-likelihood image reconstruction for digital holography,” J. Opt. Soc. Am. A 21(5), 737–750 (2004).
[Crossref] [PubMed]

F. Dubois, M. L. Requena, C. Minetti, O. Monnom, and E. Istasse, “Partial spatial coherence effects in digital holographic microscopy with a laser source,” Appl. Opt. 43(5), 1131–1139 (2004).
[Crossref] [PubMed]

Y. Sun, S. Duthaler, and B. J. Nelson, “Autofocusing in computer microscopy: selecting the optimal focus algorithm,” Microsc. Res. Tech. 65(3), 139–149 (2004).
[Crossref] [PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

2000 (1)

1995 (2)

D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Am. Stat. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41(3), 613–627 (1995).
[Crossref]

1989 (1)

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Contr. Signals Syst. 2(4), 303–314 (1989).
[Crossref]

1969 (1)

E. Wolf, “Three-dimensional structure determination of semi-transparent objects from holographic data,” Opt. Commun. 1(4), 153–156 (1969).
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Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
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Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
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J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
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M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
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Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
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Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
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Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
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J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
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J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
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Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park, “Quantitative Phase Imaging and Artificial Intelligence: A Review,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–14 (2019).
[Crossref]

G. Kim, Y. Jo, H. Cho, H. S. Min, and Y. Park, “Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,” Biosens. Bioelectron. 123, 69–76 (2019).
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J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
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S. A. Yang, J. Yoon, K. Kim, and Y. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease,” Cytometry A 91(5), 510–518 (2017).
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S. Shin, K. Kim, K. Lee, S. Lee, and Y. Park, “Effects of spatiotemporal coherence on interferometric microscopy,” Opt. Express 25(7), 8085–8097 (2017).
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M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
[Crossref] [PubMed]

J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
[Crossref] [PubMed]

K. Kim, K. Choe, I. Park, P. Kim, and Y. Park, “Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice,” Sci. Rep. 6(1), 33084 (2016).
[Crossref] [PubMed]

H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
[Crossref] [PubMed]

J. Yoon, K. Kim, H. Park, C. Choi, S. Jang, and Y. Park, “Label-free characterization of white blood cells by measuring 3D refractive index maps,” Biomed. Opt. Express 6(10), 3865–3875 (2015).
[Crossref] [PubMed]

Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
[Crossref] [PubMed]

Kim, M. H.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
[Crossref] [PubMed]

Kim, P.

K. Kim, K. Choe, I. Park, P. Kim, and Y. Park, “Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice,” Sci. Rep. 6(1), 33084 (2016).
[Crossref] [PubMed]

Kim, Y.

H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
[Crossref] [PubMed]

Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
[Crossref] [PubMed]

Kim, Y.-J.

Kiss, R.

F. Dubois, C. Yourassowsky, O. Monnom, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
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Lee, C.-G.

J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
[Crossref] [PubMed]

Lee, E.

M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
[Crossref] [PubMed]

Lee, J.

Lee, K.

Lee, M.

J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
[Crossref] [PubMed]

M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
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Lee, S.

J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
[Crossref] [PubMed]

G. Kim, S. Lee, S. Shin, and Y. Park, “Three-dimensional label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography,” Sci. Rep. 8(1), 1782 (2018).
[Crossref] [PubMed]

S. Shin, K. Kim, K. Lee, S. Lee, and Y. Park, “Effects of spatiotemporal coherence on interferometric microscopy,” Opt. Express 25(7), 8085–8097 (2017).
[Crossref] [PubMed]

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
[Crossref]

M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
[Crossref] [PubMed]

J. Lim, K. Lee, K. H. Jin, S. Shin, S. Lee, Y. Park, and J. C. Ye, “Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography,” Opt. Express 23(13), 16933–16948 (2015).
[Crossref] [PubMed]

Lee, S. Y.

Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park, “Quantitative Phase Imaging and Artificial Intelligence: A Review,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–14 (2019).
[Crossref]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Lee, S.-E.

H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
[Crossref] [PubMed]

Lee, W.-J.

H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
[Crossref] [PubMed]

Legros, J.-C.

F. Dubois, C. Yourassowsky, O. Monnom, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

Li, S.

Li, X.

Li, Y.

Lim, J.

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]

Liu, Z. B.

Loterie, D.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7(1), 69 (2018).
[Crossref] [PubMed]

Maas, A. L.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in InProceedings of the International Conference on Machine Learning, 2013), 3.

Macias, V.

T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
[Crossref] [PubMed]

Mahmood, K.

Maleckar, M. M.

C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nat. Methods 15(11), 917–920 (2018).
[Crossref] [PubMed]

Markman, A.

Marquet, P.

Massaga, J. J.

J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
[Crossref] [PubMed]

Matemba, L. E.

J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
[Crossref] [PubMed]

Melamed, J.

T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
[Crossref] [PubMed]

Memmolo, P.

Min, H.

Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park, “Quantitative Phase Imaging and Artificial Intelligence: A Review,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–14 (2019).
[Crossref]

Min, H. S.

G. Kim, Y. Jo, H. Cho, H. S. Min, and Y. Park, “Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,” Biosens. Bioelectron. 123, 69–76 (2019).
[Crossref] [PubMed]

Minetti, C.

Mishra, A.

Monnom, O.

F. Dubois, C. Yourassowsky, O. Monnom, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

F. Dubois, M. L. Requena, C. Minetti, O. Monnom, and E. Istasse, “Partial spatial coherence effects in digital holographic microscopy with a laser source,” Appl. Opt. 43(5), 1131–1139 (2004).
[Crossref] [PubMed]

Moser, C.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7(1), 69 (2018).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Nehmetallah, G.

Nelson, B. J.

Y. Sun, S. Duthaler, and B. J. Nelson, “Autofocusing in computer microscopy: selecting the optimal focus algorithm,” Microsc. Res. Tech. 65(3), 139–149 (2004).
[Crossref] [PubMed]

Nelson, 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]

Ng, A. Y.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in InProceedings of the International Conference on Machine Learning, 2013), 3.

Nguyen, T.

Nguyen, T. H.

T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Ounkomol, C.

C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nat. Methods 15(11), 917–920 (2018).
[Crossref] [PubMed]

Ozcan, A.

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]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

Park, H.

Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
[Crossref] [PubMed]

H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
[Crossref] [PubMed]

J. Yoon, K. Kim, H. Park, C. Choi, S. Jang, and Y. Park, “Label-free characterization of white blood cells by measuring 3D refractive index maps,” Biomed. Opt. Express 6(10), 3865–3875 (2015).
[Crossref] [PubMed]

Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
[Crossref] [PubMed]

Park, I.

K. Kim, K. Choe, I. Park, P. Kim, and Y. Park, “Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice,” Sci. Rep. 6(1), 33084 (2016).
[Crossref] [PubMed]

Park, S.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

Park, T.

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” Proc. IEEE Int. Conf. Comput. Vis. 2018, 2242–2251 (2017).
[Crossref]

Park, Y.

G. Kim, Y. Jo, H. Cho, H. S. Min, and Y. Park, “Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,” Biosens. Bioelectron. 123, 69–76 (2019).
[Crossref] [PubMed]

Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park, “Quantitative Phase Imaging and Artificial Intelligence: A Review,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–14 (2019).
[Crossref]

G. Kim, S. Lee, S. Shin, and Y. Park, “Three-dimensional label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography,” Sci. Rep. 8(1), 1782 (2018).
[Crossref] [PubMed]

J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
[Crossref] [PubMed]

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12(10), 578–589 (2018).
[Crossref]

S. A. Yang, J. Yoon, K. Kim, and Y. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease,” Cytometry A 91(5), 510–518 (2017).
[Crossref] [PubMed]

S. Shin, K. Kim, K. Lee, S. Lee, and Y. Park, “Effects of spatiotemporal coherence on interferometric microscopy,” Opt. Express 25(7), 8085–8097 (2017).
[Crossref] [PubMed]

I. Choi, K. Lee, and Y. Park, “Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration,” Opt. Express 25(24), 30771–30779 (2017).
[Crossref] [PubMed]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
[Crossref]

J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
[Crossref] [PubMed]

M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
[Crossref] [PubMed]

K. Kim, K. Choe, I. Park, P. Kim, and Y. Park, “Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice,” Sci. Rep. 6(1), 33084 (2016).
[Crossref] [PubMed]

J. Yoon, K. Kim, H. Park, C. Choi, S. Jang, and Y. Park, “Label-free characterization of white blood cells by measuring 3D refractive index maps,” Biomed. Opt. Express 6(10), 3865–3875 (2015).
[Crossref] [PubMed]

H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
[Crossref] [PubMed]

Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
[Crossref] [PubMed]

J. Lim, K. Lee, K. H. Jin, S. Shin, S. Lee, Y. Park, and J. C. Ye, “Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography,” Opt. Express 23(13), 16933–16948 (2015).
[Crossref] [PubMed]

Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
[Crossref] [PubMed]

Y. Park, W. Choi, Z. Yaqoob, R. Dasari, K. Badizadegan, and M. S. Feld, “Speckle-field digital holographic microscopy,” Opt. Express 17(15), 12285–12292 (2009).
[Crossref] [PubMed]

Paturzo, M.

Pla, F.

Poon, T.-C.

Popescu, G.

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12(10), 578–589 (2018).
[Crossref]

T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
[Crossref] [PubMed]

Poplin, R.

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]

Psaltis, D.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7(1), 69 (2018).
[Crossref] [PubMed]

Rahmani, B.

B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, and C. Moser, “Multimode optical fiber transmission with a deep learning network,” Light Sci. Appl. 7(1), 69 (2018).
[Crossref] [PubMed]

Rawat, S.

Requena, M. L.

Rivenson, Y.

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]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

Rohith, T. M.

Rubin, L. L.

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]

Seshamani, S.

C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nat. Methods 15(11), 917–920 (2018).
[Crossref] [PubMed]

Shah, 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]

Shahbazmohamadi, S.

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Shim, H.

Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
[Crossref] [PubMed]

Shin, S.

J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
[Crossref] [PubMed]

G. Kim, S. Lee, S. Shin, and Y. Park, “Three-dimensional label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography,” Sci. Rep. 8(1), 1782 (2018).
[Crossref] [PubMed]

S. Shin, K. Kim, K. Lee, S. Lee, and Y. Park, “Effects of spatiotemporal coherence on interferometric microscopy,” Opt. Express 25(7), 8085–8097 (2017).
[Crossref] [PubMed]

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
[Crossref]

J. Lim, K. Lee, K. H. Jin, S. Shin, S. Lee, Y. Park, and J. C. Ye, “Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography,” Opt. Express 23(13), 16933–16948 (2015).
[Crossref] [PubMed]

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Sinha, A.

Skibinski, G.

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]

Son, K.

Sotthivirat, S.

Sridharan, S.

T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
[Crossref] [PubMed]

Sun, Y.

Y. Sun, S. Duthaler, and B. J. Nelson, “Autofocusing in computer microscopy: selecting the optimal focus algorithm,” Microsc. Res. Tech. 65(3), 139–149 (2004).
[Crossref] [PubMed]

Tan, J. B.

Tao, Z.

Teng, D.

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]

Tian, L.

Toh, H. T.

Tulino, A. M.

Van Ham, P.

F. Dubois, C. Yourassowsky, O. Monnom, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

Vo, H.

Wang, H.

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Wolf, E.

E. Wolf, “Three-dimensional structure determination of semi-transparent objects from holographic data,” Opt. Commun. 1(4), 153–156 (1969).
[Crossref]

Xue, Y.

Yamashita, Y.

Yamauchi, T.

Yang, H.

Yang, S. A.

S. A. Yang, J. Yoon, K. Kim, and Y. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease,” Cytometry A 91(5), 510–518 (2017).
[Crossref] [PubMed]

Yang, S. J.

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]

Yang, S.-A.

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
[Crossref]

Yaqoob, Z.

Ye, J. C.

Yoon, H. S.

Yoon, J.

S. A. Yang, J. Yoon, K. Kim, and Y. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease,” Cytometry A 91(5), 510–518 (2017).
[Crossref] [PubMed]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
[Crossref]

M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
[Crossref] [PubMed]

J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
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J. Yoon, K. Kim, H. Park, C. Choi, S. Jang, and Y. Park, “Label-free characterization of white blood cells by measuring 3D refractive index maps,” Biomed. Opt. Express 6(10), 3865–3875 (2015).
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M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
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Zhu, J.-Y.

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” Proc. IEEE Int. Conf. Comput. Vis. 2018, 2242–2251 (2017).
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P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in InProceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2017), pp. 1063–6919.

Appl. Opt. (6)

Biomed. Opt. Express (1)

Biosens. Bioelectron. (1)

G. Kim, Y. Jo, H. Cho, H. S. Min, and Y. Park, “Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,” Biosens. Bioelectron. 123, 69–76 (2019).
[Crossref] [PubMed]

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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Cytometry A (1)

S. A. Yang, J. Yoon, K. Kim, and Y. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease,” Cytometry A 91(5), 510–518 (2017).
[Crossref] [PubMed]

IEEE J. Sel. Top. Quantum Electron. (1)

Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park, “Quantitative Phase Imaging and Artificial Intelligence: A Review,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–14 (2019).
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IEEE Trans. Image Process. (2)

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T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu, “Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning,” J. Biomed. Opt. 22(3), 36015 (2017).
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F. Dubois, C. Yourassowsky, O. Monnom, J.-C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

J. Biomed. Photonics Eng. (1)

K. Kim, J. Yoon, S. Shin, S. Lee, S.-A. Yang, and Y. Park, “Optical diffraction tomography techniques for the study of cell pathophysiology,” J. Biomed. Photonics Eng. 2(2), 2994 (2016).
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J. Opt. Soc. Am. A (1)

Light Sci. Appl. (3)

V. Bianco, P. Memmolo, M. Paturzo, A. Finizio, B. Javidi, and P. Ferraro, “Quasi noise-free digital holography,” Light Sci. Appl. 5(9), e16142 (2016).
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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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C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nat. Methods 15(11), 917–920 (2018).
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Nat. Photonics (1)

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12(10), 578–589 (2018).
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Opt. Express (8)

J. Lim, K. Lee, K. H. Jin, S. Shin, S. Lee, Y. Park, and J. C. Ye, “Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography,” Opt. Express 23(13), 16933–16948 (2015).
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Y. Park, W. Choi, Z. Yaqoob, R. Dasari, K. Badizadegan, and M. S. Feld, “Speckle-field digital holographic microscopy,” Opt. Express 17(15), 12285–12292 (2009).
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S. Shin, K. Kim, K. Lee, S. Lee, and Y. Park, “Effects of spatiotemporal coherence on interferometric microscopy,” Opt. Express 25(7), 8085–8097 (2017).
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H. Farrokhi, J. Boonruangkan, B. J. Chun, T. M. Rohith, A. Mishra, H. T. Toh, H. S. Yoon, and Y.-J. Kim, “Speckle reduction in quantitative phase imaging by generating spatially incoherent laser field at electroactive optical diffusers,” Opt. Express 25(10), 10791–10800 (2017).
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I. Choi, K. Lee, and Y. Park, “Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration,” Opt. Express 25(24), 30771–30779 (2017).
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P. Memmolo, I. Esnaola, A. Finizio, M. Paturzo, P. Ferraro, and A. M. Tulino, “SPADEDH: a sparsity-based denoising method of digital holograms without knowing the noise statistics,” Opt. Express 20(15), 17250–17257 (2012).
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Y. Jo, J. Jung, M. H. Kim, H. Park, S.-J. Kang, and Y. Park, “Label-free identification of individual bacteria using Fourier transform light scattering,” Opt. Express 23(12), 15792–15805 (2015).
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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).
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Opt. Lett. (2)

Optica (3)

Proc. IEEE Int. Conf. Comput. Vis. (1)

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” Proc. IEEE Int. Conf. Comput. Vis. 2018, 2242–2251 (2017).
[Crossref]

Sci. Adv. (1)

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
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Sci. Rep. (7)

G. Kim, S. Lee, S. Shin, and Y. Park, “Three-dimensional label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography,” Sci. Rep. 8(1), 1782 (2018).
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Y. Kim, H. Shim, K. Kim, H. Park, S. Jang, and Y. Park, “Profiling individual human red blood cells using common-path diffraction optical tomography,” Sci. Rep. 4(1), 6659 (2014).
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M. Lee, E. Lee, J. Jung, H. Yu, K. Kim, J. Yoon, S. Lee, Y. Jeong, and Y. Park, “Label-free optical quantification of structural alterations in Alzheimer’s disease,” Sci. Rep. 6(1), 31034 (2016).
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J. Jung, L. E. Matemba, K. Lee, P. E. Kazyoba, J. Yoon, J. J. Massaga, K. Kim, D.-J. Kim, and Y. Park, “Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,” Sci. Rep. 6(1), 31698 (2016).
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H. Park, S.-H. Hong, K. Kim, S.-H. Cho, W.-J. Lee, Y. Kim, S.-E. Lee, and Y. Park, “Characterizations of individual mouse red blood cells parasitized by Babesia microti using 3-D holographic microscopy,” Sci. Rep. 5(1), 10827 (2015).
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J. Jung, S.-J. Hong, H.-B. Kim, G. Kim, M. Lee, S. Shin, S. Lee, D.-J. Kim, C.-G. Lee, and Y. Park, “Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography,” Sci. Rep. 8(1), 6524 (2018).
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K. Kim, K. Choe, I. Park, P. Kim, and Y. Park, “Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice,” Sci. Rep. 6(1), 33084 (2016).
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J. Yoon, Y. Jo, Y. S. Kim, Y. Yu, J. Park, S. Lee, W. S. Park, and Y. Park, “Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning,” JoVE, e58305 (2018).
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Figures (13)

Fig. 1
Fig. 1 Coherent noise problem in optical diffraction tomography (ODT). (a-b) The ODT employs angularly varying illumination to capture off-axis holograms. (c) Each complex optical field is reconstructed from the obtained holograms. (d) 2D sliced image of 3D reconstructed tomogram at Δz = 3.9 μm e) 2D sliced image of 3D reconstructed tomogram at focus Δz = 0 μm corrupted with the coherent noise. (f) 3D rendering of the whole reconstructed tomogram.
Fig. 2
Fig. 2 Overview of the present network for de-noising: training and testing (a) Two classes of data set for training were prepared. xiX: noisy tomogram and yiY: clean tomogram. (b) Training process of the proposed network. GXY: Generator that maps x to y. GYX: Generator that maps y to x. Dy: Discriminator to determine if given input is a generated clean image from GXY or a real data y. DX: Discriminator to determine if given input is a generated noisy image from GYX or a real data x. L D Y :Adversarial loss for DY. L D X :Adversarial loss for DY. L C X :cycle-consistency loss for x. L C Y :cycle-consistency loss for y. (c) Trained network, GXY removes the coherent noise of 2D sliced tomogram.
Fig. 3
Fig. 3 Architecture search for our deep learning model. (Top row) (a) Original tomogram before denoising. (b) Denoised tomogram image using the Naïve-Unet l1 loss function. The detail of subcellular features is not resolved and the yellow colored artifact is shown, as marked by the arrow. The checkerboard effect also appears and can be visualized in Fourier domain. (c) The result of resize-U-net l1 loss improves the image quality by eliminating the checkerboard. (d) To preserve details of spatial features, structure similarity index map loss was utilized, along with the resize-U-net, resolving the subcellular features and clearer boundaries of nucleoli. (Bottom row) Each Fourier spectrum of the corresponding tomogram is shown. The black-dotted circle indicates the numerical aperture of the imaging system.
Fig. 4
Fig. 4 Quantitative analysis of the proposed network. (a) Original tomogram of the silica microbead degraded by the coherent noise. (b) Tomogram denoised via our method. (c) 2D tomogram slices in the background region (number of slices = 11), marked by top-left corner box, acquired in the axial direction; the RI distributions are shown for comparison to highlight the denoising effect. (d) Line profiles along the horizontal way are visualized.
Fig. 5
Fig. 5 Comparison to non-data-driven approaches. (a) Original 2D tomogram of a HeLa cell. (b) Denoised tomogram using the present method. (c) Denoised tomograms using BM3D (Block Matching 3D denoising). (d) Denoised tomogram using TV (Total Variation) minimization. (e) Denoised tomogram using Haar wavelet shrinkage. We varied the core parameter of each algorithm: σ is standard deviation of Gaussian distribution for the BM3D (1, 5, and 40); λ is regularization weight between a minimization term and a TV term for the TV denoising (0.1, 0.5, 10); τ is a thresholding value for the wavelet shrinkage (0.0001, 0.0028, and 0.1).
Fig. 6
Fig. 6 Experimental validation of the present method. Tomograms of NIH3T3, MDA231, and HeLa (a) in the presence of coherent noise, in the shape of the fringe pattern and (b) after coherent noise removal using our trained network.
Fig. 7
Fig. 7 Time-lapse experiment of HeLa cells with a time interval 10 mins for 30 mins. (a) Original tomograms with incremental coherent noise, induced by focal drift. (b) Our method effectively removes the noises. (c) Comparison of the cropped background regions. (d) Noise level in the regions displayed in (c). It is quantified by standard deviation and decreases upon the application of our method.
Fig. 8
Fig. 8 Optical setup of optical diffraction tomography. FC: Fiber coupler, L: Lens, M: Mirror, DMD: Digital micromirror device, OL: Objective lens, S: Sample, BS: Beam splitter, and CAM: Camera.
Fig. 9
Fig. 9 Architecture of generator. LeakyReLU: Leaky rectified non-linear unit. Conv2D: two-dimensional (2-D) convolutional operation. BatchNorm2D: 2-D batch normalization. Tanh: Tanh nonlinearity. The architecture is based on Unet that relates one image domain to the other image domain.
Fig. 10
Fig. 10 Architecture of discriminator. Conv2D: two dimensional (2D) convolutional operation. BatchNorm2D: 2D batch normalization. LeakyRelu: Leaky rectified non-linear unit. AveragePool: Pooling image by average. The discriminator outputs a single scalar that determines the input image obtained from the generator part as real or fake.
Fig. 11
Fig. 11 Annotation standard. For definite transform between two image domains, we perceptually annotated (a) clean and (b) noisy tomograms to prepare the training set and removed (c) tomograms with weak fringes, which is ambiguous to classify.
Fig. 12
Fig. 12 Failure cases for denoising: original tomograms of biological samples (First row) are processed to denoised tomograms (Second row), using the naïve denoising network. (a) The network does not fully remove the strong pattern and clearly visible noises remain in the denoised tomogram. (b) The network that learns particular cell features can generate weird artifacts in the denoised tomogram. (c) Non-coherent noises are not removed in the denoised tomogram, as indicated by arrows.
Fig. 13
Fig. 13 Identity mapping of the network. (First row) noisy tomogram from data set passes through the denoising network and noising network then. x: noisy tomogram, GXY(x): processed tomogram using the denoising network, GYX(GXY(x)): identity-mapped noisy tomogram using the noising network. (Second row) clean tomogram from data set passes through the noising network and denoising network, sequentially. y: clean tomogram, GYX(y): processed tomogram using the noising network, GXY(GYX(y)): identity-mapped clean tomogram using the noising network. In the last column, two errors related to the cycle-consistent losses are displayed. The cycle-consistent losses utilizing the identity mapping boost successful training of the network.

Equations (8)

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L D Y ( G XY , D Y ,X,Y)= Ε y~ P data(y) [log( D Y (y)]+ Ε x~ P data(x) [log(1 D Y ( G XY (x))],
min G XY max D Y L D Y ( G XY , D Y ,X,Y).
L D X ( G YX , D X ,X,Y)= Ε x~ P data(x) [log D X (x)]+ Ε y~ P data(y) [log(1 D X ( G YX (y)))],
min G YX max D X L D X ( G YX , D X ,X,Y).
L C X ( G XY , G YX )= Ε x~ P data(x) [ G YX ( G XY (x))x 1 ].
L C y ( G XY , G YX )= Ε y~ P data(y) [ G XY ( G YX (y))y 1 ].
L( G XY , G YX , D X , D Y )= L D Y ( G XY , D Y ,X,Y)+ L D X ( G YX , D X ,X,Y) +λ×( L C X ( G XY , G YX )+ L C Y ( G XY , G YX )),
G * XY , G * YX = argmin G XY , G YX argmax D X , D Y L( G XY , G YX , D X , D Y ),

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