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  • Received: Jan. 3, 2020

    Accepted: Mar. 4, 2020

    Posted: Mar. 25, 2020

    Published Online: Mar. 25, 2020

    The Author Email: Lim Joowon (limjoowon@gmail.com), Ayoub Ahmed B. (ahmed.ayoub@epfl.ch), Psaltis Demetri (demetri.psaltis@epfl.ch)

    DOI: 10.1117/1.AP.2.2.026001

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    Joowon Lim, Ahmed B. Ayoub, Demetri Psaltis. Three-dimensional tomography of red blood cells using deep learning[J]. Advanced Photonics, 2020, 2(2): 026001

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[1] M. Born, E. Wolf. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light(1999).

[2] K. Lee, et al.. Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications. Sensors, 13, 4170-4191(2013).

[3] K. Kim, et al.. Optical diffraction tomography techniques for the study of cell pathophysiology. J. Biomed. Photonics Eng., 2, 020201(2016).

[4] D. Jin, et al.. Tomographic phase microscopy: principles and applications in bioimaging. J. Opt. Soc. Am. B, 34, B64-B77(2017).

[5] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photonics, 12, 578-589(2018).

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

[7] J. Lim, et al.. Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography. Opt. Express, 23, 16933-16948(2015).

[8] Y. Sung, R. R. Dasari. Deterministic regularization of three-dimensional optical diffraction tomography. J. Opt. Soc. Am. A, 28, 1554-1561(2011).

[9] Y. Rivenson, et al.. Deep learning microscopy. Optica, 4, 1437-1443(2017).

[10] A. Sinha, et al.. Lensless computational imaging through deep learning. Optica, 4, 1117-1125(2017).

[11] Y. Rivenson, et al.. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light: Sci. Appl., 7, 17141(2018).

[12] Y. Rivenson, et al.. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng., 3, 466-477(2019).

[13] N. Borhani, et al.. Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy. Biomed. Opt. Express, 10, 1339-1350(2019).

[14] Y. Jo, et al.. Holographic deep learning for rapid optical screening of anthrax spores. Sci. Adv., 3, e1700606(2017).

[15] J. Yoon, et al.. Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning. Sci. Rep., 7, 6654(2017).

[16] J. Lee, et al.. Deep-learning-based label-free segmentation of cell nuclei in time-lapse refractive index tomograms. IEEE Access, 7, 83449-83460(2019).

[17] Y. Jo, et al.. Quantitative phase imaging and artificial intelligence: a review. IEEE J. Sel. Top. Quantum Electron., 25, 6800914(2018).

[18] J. Yoo, et al.. Deep learning diffuse optical tomography. IEEE Trans. Med. Imaging(2019).

[19] Y. Sun, Z. Xia, U. S. Kamilov. Efficient and accurate inversion of multiple scattering with deep learning. Opt. Express, 26, 14678-14688(2018).

[20] T. C. Nguyen, V. Bui, G. Nehmetallah. Computational optical tomography using 3-D deep convolutional neural networks. Opt. Eng., 57, 043111(2018).

[21] A. Goy, et al.. High-resolution limited-angle phase tomography of dense layered objects using deep neural networks. Proc. Natl. Acad. Sci. U. S. A., 116, 19848-19856(2019).

[22] K. Kim, et al.. High-resolution three-dimensional imaging of red blood cells parasitized by Plasmodium falciparum and in situ hemozoin crystals using optical diffraction tomography. J. Biomed. Opt., 19, 011005(2013).

[23] Y. Kim, et al.. Profiling individual human red blood cells using common-path diffraction optical tomography. Sci. Rep., 4, 6659(2014).

[24] M. A. Yurkin, et al.. Discrete Dipole Simulations of Light Scattering by Blood Cells(2007).

[25] M. A. Yurkin, A. G. Hoekstra. The discrete-dipole-approximation code ADDA: capabilities and known limitations. J. Quant. Spectrosc. Radiat. Transfer, 112, 2234-2247(2011).

[26] S. D’Agostino, et al.. Enhanced fluorescence by metal nanospheres on metal substrates. Opt. Lett., 34, 2381-2383(2009).

[27] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015).

[28] K. H. Jin, et al.. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process., 26, 4509-4522(2017).

[29] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

[30] T. Salimans, D. P. Kingma. Weight normalization: a simple reparameterization to accelerate training of deep neural networks, 901-909(2016).

[31] J. L. Ba, J. R. Kiros, G. E. Hinton. Layer normalization(2016).

[32] A. B. Ayoub, et al.. A method for assessing the fidelity of optical diffraction tomography reconstruction methods using structured illumination. Opt. Commun., 454, 124486(2020).

[33] J. Lim, et al.. High-fidelity optical diffraction tomography of multiple scattering samples. Light: Sci. Appl., 8, 82(2019).

[34] I. Udroiu. Estimation of erythrocyte surface area in mammals(2014).

[35] K. Namdee, et al.. Effect of variation in hemorheology between human and animal blood on the binding efficacy of vascular-targeted carriers. Sci. Rep., 5, 11631(2015).

[36] N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9, 62-66(1979).

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