Abstract

We present a study on lung squamous cell carcinoma diagnosis using quantitative TI-DIC microscopy and a deep convolutional neural network (DCNN). The 2-D phase map of unstained tissue sections is first retrieved from through-focus differential interference contrast (DIC) images based on the transport of intensity equation (TIE). The spatially resolved optical properties are then computed from the 2-D phase map via the scattering-phase theorem. The scattering coefficient (μS) and the reduced scattering coefficient (μS') are found to increase whereas the anisotropy factor (g) is found to decrease with cancer. A DCNN classifier is developed afterwards to classify the tissue using either the DIC images or 2-D optical property maps of μS, μS' and g. The DCNN classifier with the optical property maps exhibits high accuracy, significantly outperforming the same DCNN classifier on the DIC images. The label-free quantitative phase microscopy together with deep learning may emerge as a promising approach for in situ rapid cancer diagnosis.

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

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2018 (1)

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

2017 (4)

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

M. Xu, “Plum pudding random medium model of biological tissue toward remote microscopy from spectroscopic light scattering,” Biomed. Opt. Express 8(6), 2879–2895 (2017).
[Crossref] [PubMed]

Z. Xu, M. Reilley, R. Li, and M. Xu, “Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy,” J. Biomed. Opt. 22(6), 066009 (2017).
[Crossref] [PubMed]

T. Novikova, “Optical techniques for cervical neoplasia detection,” Beilstein J. Nanotechnol. 8(8), 1844–1862 (2017).
[Crossref] [PubMed]

2016 (1)

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

2015 (1)

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

2013 (1)

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

2012 (1)

S. Magalhães Barros Netto, A. Corrêa Silva, R. Acatauassú Nunes, and M. Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med. 42(11), 1110–1121 (2012).
[Crossref] [PubMed]

2011 (4)

M. Xu, “Scattering-phase theorem: anomalous diffraction by forward-peaked scattering media,” Opt. Express 19(22), 21643–21651 (2011).
[Crossref] [PubMed]

Z. Wang, K. Tangella, A. Balla, and G. Popescu, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16(11), 116017 (2011).
[Crossref] [PubMed]

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

2010 (1)

2008 (2)

M. Xu, T. T. Wu, and J. Y. Qu, “Unified Mie and fractal scattering by cells and experimental study on application in optical characterization of cellular and subcellular structures,” J. Biomed. Opt. 13(2), 024015 (2008).
[Crossref] [PubMed]

N. Thekkek and R. Richards-Kortum, “Optical imaging for cervical cancer detection: solutions for a continuing global problem,” Nat. Rev. Cancer 8(9), 725–731 (2008).
[Crossref] [PubMed]

2007 (1)

2005 (1)

2003 (1)

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

2001 (1)

L. J. Allen and M. P. Oxley, “Phase retrieval from series of images obtained by defocus variation,” Opt. Commun. 199(1–4), 65–75 (2001).
[Crossref]

2000 (1)

N. Ramanujam, “Fluorescence spectroscopy of neoplastic and non-neoplastic tissues,” Neoplasia 2(1-2), 89–117 (2000).
[Crossref] [PubMed]

Acatauassú Nunes, R.

S. Magalhães Barros Netto, A. Corrêa Silva, R. Acatauassú Nunes, and M. Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med. 42(11), 1110–1121 (2012).
[Crossref] [PubMed]

Alfano, R. R.

Allen, L. J.

L. J. Allen and M. P. Oxley, “Phase retrieval from series of images obtained by defocus variation,” Opt. Commun. 199(1–4), 65–75 (2001).
[Crossref]

Anguelov, D.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Arzumanov, G.

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

Backman, V.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Balla, A.

Z. Wang, K. Tangella, A. Balla, and G. Popescu, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16(11), 116017 (2011).
[Crossref] [PubMed]

Barbastathis, G.

Bengio, Y.

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

Bista, R. K.

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

Boone, C. W.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Brand, R. E.

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

Cai, H.

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Cai, S.

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

Chen, A. Y.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Corrêa Silva, A.

S. Magalhães Barros Netto, A. Corrêa Silva, R. Acatauassú Nunes, and M. Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med. 42(11), 1110–1121 (2012).
[Crossref] [PubMed]

Dasari, R. R.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

DeAngelo, B.

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

Egashira, R.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

El-Deiry, M. W.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Erhan, D.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Fei, B.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Feld, M. S.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Fuentes, C.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Fukuoka, J.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Gattass, M.

S. Magalhães Barros Netto, A. Corrêa Silva, R. Acatauassú Nunes, and M. Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med. 42(11), 1110–1121 (2012).
[Crossref] [PubMed]

Georgakoudi, I.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Griffith, C. C.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Halicek, M.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Hartman, D. J.

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

Hayashi, T.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Hinton, G.

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

Hori, T.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Iftikhar, M.

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

Jia, Y.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Jin, C.

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Kabani, S.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Kashima, Y.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Kou, S. S.

Laver, N.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

LeCun, Y.

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

Li, L.

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Li, R.

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

Z. Xu, M. Reilley, R. Li, and M. Xu, “Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy,” J. Biomed. Opt. 22(6), 066009 (2017).
[Crossref] [PubMed]

Little, J. V.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Liu, W.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Liu, Y.

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

Lu, G.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Magalhães Barros Netto, S.

S. Magalhães Barros Netto, A. Corrêa Silva, R. Acatauassú Nunes, and M. Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med. 42(11), 1110–1121 (2012).
[Crossref] [PubMed]

Müller, M. G.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Nakayama, T.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Novikova, T.

T. Novikova, “Optical techniques for cervical neoplasia detection,” Beilstein J. Nanotechnol. 8(8), 1844–1862 (2017).
[Crossref] [PubMed]

Nunomura, S.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Oxley, M. P.

L. J. Allen and M. P. Oxley, “Phase retrieval from series of images obtained by defocus variation,” Opt. Commun. 199(1–4), 65–75 (2001).
[Crossref]

Patel, M.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Popescu, G.

Z. Wang, K. Tangella, A. Balla, and G. Popescu, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16(11), 116017 (2011).
[Crossref] [PubMed]

Qu, J. Y.

M. Xu, T. T. Wu, and J. Y. Qu, “Unified Mie and fractal scattering by cells and experimental study on application in optical characterization of cellular and subcellular structures,” J. Biomed. Opt. 13(2), 024015 (2008).
[Crossref] [PubMed]

T. T. Wu, J. Y. Qu, and M. Xu, “Unified Mie and fractal scattering by biological cells and subcellular structures,” Opt. Lett. 32(16), 2324–2326 (2007).
[Crossref] [PubMed]

Rabinovich, A.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Ramanujam, N.

N. Ramanujam, “Fluorescence spectroscopy of neoplastic and non-neoplastic tissues,” Neoplasia 2(1-2), 89–117 (2000).
[Crossref] [PubMed]

Reed, S.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Reilley, M.

Z. Xu, M. Reilley, R. Li, and M. Xu, “Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy,” J. Biomed. Opt. 22(6), 066009 (2017).
[Crossref] [PubMed]

Richards-Kortum, R.

N. Thekkek and R. Richards-Kortum, “Optical imaging for cervical cancer detection: solutions for a continuing global problem,” Nat. Rev. Cancer 8(9), 725–731 (2008).
[Crossref] [PubMed]

Sano, H.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Sermanet, P.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Shanley, P.

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

Shapshay, S. M.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Sheppard, C. J.

Szegedy, C.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Tabata, K.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Tan, W.

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Tanaka, T.

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Tangella, K.

Z. Wang, K. Tangella, A. Balla, and G. Popescu, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16(11), 116017 (2011).
[Crossref] [PubMed]

Thekkek, N.

N. Thekkek and R. Richards-Kortum, “Optical imaging for cervical cancer detection: solutions for a continuing global problem,” Nat. Rev. Cancer 8(9), 725–731 (2008).
[Crossref] [PubMed]

Uttam, S.

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

Valdez, T. A.

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Vanhoucke, V.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

Waller, L.

Wang, J.

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Wang, X.

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

Wang, Z.

Z. Wang, K. Tangella, A. Balla, and G. Popescu, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16(11), 116017 (2011).
[Crossref] [PubMed]

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Wu, T. T.

M. Xu, T. T. Wu, and J. Y. Qu, “Unified Mie and fractal scattering by cells and experimental study on application in optical characterization of cellular and subcellular structures,” J. Biomed. Opt. 13(2), 024015 (2008).
[Crossref] [PubMed]

T. T. Wu, J. Y. Qu, and M. Xu, “Unified Mie and fractal scattering by biological cells and subcellular structures,” Opt. Lett. 32(16), 2324–2326 (2007).
[Crossref] [PubMed]

Xu, M.

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

Z. Xu, M. Reilley, R. Li, and M. Xu, “Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy,” J. Biomed. Opt. 22(6), 066009 (2017).
[Crossref] [PubMed]

M. Xu, “Plum pudding random medium model of biological tissue toward remote microscopy from spectroscopic light scattering,” Biomed. Opt. Express 8(6), 2879–2895 (2017).
[Crossref] [PubMed]

M. Xu, “Scattering-phase theorem: anomalous diffraction by forward-peaked scattering media,” Opt. Express 19(22), 21643–21651 (2011).
[Crossref] [PubMed]

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

M. Xu, T. T. Wu, and J. Y. Qu, “Unified Mie and fractal scattering by cells and experimental study on application in optical characterization of cellular and subcellular structures,” J. Biomed. Opt. 13(2), 024015 (2008).
[Crossref] [PubMed]

T. T. Wu, J. Y. Qu, and M. Xu, “Unified Mie and fractal scattering by biological cells and subcellular structures,” Opt. Lett. 32(16), 2324–2326 (2007).
[Crossref] [PubMed]

M. Xu and R. R. Alfano, “Fractal mechanisms of light scattering in biological tissue and cells,” Opt. Lett. 30(22), 3051–3053 (2005).
[Crossref] [PubMed]

Xu, Z.

Z. Xu, M. Reilley, R. Li, and M. Xu, “Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy,” J. Biomed. Opt. 22(6), 066009 (2017).
[Crossref] [PubMed]

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

Yang, X.

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Zeng, B.

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

Zheng, L.

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

Beilstein J. Nanotechnol. (1)

T. Novikova, “Optical techniques for cervical neoplasia detection,” Beilstein J. Nanotechnol. 8(8), 1844–1862 (2017).
[Crossref] [PubMed]

BioMed Res. Int. (1)

T. Hayashi, H. Sano, R. Egashira, K. Tabata, T. Tanaka, T. Nakayama, Y. Kashima, T. Hori, S. Nunomura, and J. Fukuoka, “Difference of morphology and immunophenotype between central and peripheral squamous cell carcinomas of the lung,” BioMed Res. Int. 2013(12), 157838 (2013).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Cancer (1)

M. G. Müller, T. A. Valdez, I. Georgakoudi, V. Backman, C. Fuentes, S. Kabani, N. Laver, Z. Wang, C. W. Boone, R. R. Dasari, S. M. Shapshay, and M. S. Feld, “Spectroscopic detection and evaluation of morphologic and biochemical changes in early human oral carcinoma,” Cancer 97(7), 1681–1692 (2003).
[Crossref] [PubMed]

Chin. J. Lasers (1)

S. Cai, L. Zheng, B. Zeng, R. Li, and M. Xu, “Quantitative phase imaging based on transport-of-intensity equation and differential interference contrast microscope and its application in breast cancer diagnosis,” Chin. J. Lasers 45(3), 0307015 (2018).
[Crossref]

Comput. Biol. Med. (1)

S. Magalhães Barros Netto, A. Corrêa Silva, R. Acatauassú Nunes, and M. Gattass, “Automatic segmentation of lung nodules with growing neural gas and support vector machine,” Comput. Biol. Med. 42(11), 1110–1121 (2012).
[Crossref] [PubMed]

J. Biomed. Opt. (5)

Z. Wang, K. Tangella, A. Balla, and G. Popescu, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16(11), 116017 (2011).
[Crossref] [PubMed]

S. Uttam, R. K. Bista, D. J. Hartman, R. E. Brand, and Y. Liu, “Correction of stain variations in nuclear refractive index of clinical histology specimens,” J. Biomed. Opt. 16(11), 116013 (2011).
[Crossref] [PubMed]

M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” J. Biomed. Opt. 22(6), 060503 (2017).
[Crossref] [PubMed]

M. Xu, T. T. Wu, and J. Y. Qu, “Unified Mie and fractal scattering by cells and experimental study on application in optical characterization of cellular and subcellular structures,” J. Biomed. Opt. 13(2), 024015 (2008).
[Crossref] [PubMed]

Z. Xu, M. Reilley, R. Li, and M. Xu, “Mapping absolute tissue endogenous fluorophore concentrations with chemometric wide-field fluorescence microscopy,” J. Biomed. Opt. 22(6), 066009 (2017).
[Crossref] [PubMed]

Nat. Rev. Cancer (1)

N. Thekkek and R. Richards-Kortum, “Optical imaging for cervical cancer detection: solutions for a continuing global problem,” Nat. Rev. Cancer 8(9), 725–731 (2008).
[Crossref] [PubMed]

Nature (1)

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

Neoplasia (1)

N. Ramanujam, “Fluorescence spectroscopy of neoplastic and non-neoplastic tissues,” Neoplasia 2(1-2), 89–117 (2000).
[Crossref] [PubMed]

Opt. Commun. (1)

L. J. Allen and M. P. Oxley, “Phase retrieval from series of images obtained by defocus variation,” Opt. Commun. 199(1–4), 65–75 (2001).
[Crossref]

Opt. Express (1)

Opt. Lett. (3)

Proc. SPIE (1)

M. Iftikhar, B. DeAngelo, G. Arzumanov, P. Shanley, Z. Xu, and M. Xu, “Characterizing scattering property of random media from phase map of a thin slice: the scattering-phase theorem and the intensity propagation equation approach,” Proc. SPIE 7896, 5683–5686 (2011).
[Crossref]

Sci. Rep. (1)

J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Sci. Rep. 6(1), 27327 (2016).
[Crossref] [PubMed]

Other (8)

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H. Aung, J. Buckley, P. Kostyk, B. Rodriguez, S. Phelan, and M. Xu, “Imaging three-dimensional refractive index distribution with differential interference contrast (DIC) microscopy,” Proc. SPIE 8227, 82270G (2012).

A. G. Howard, “Some improvements on deep convolutional neural network based image classification,” https://arxiv.org/abs/1312.5402 .

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: convolutional architecture for fast feature embedding,” https://arxiv.org/abs/1408.5093 .
[Crossref]

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2015), pp. 1–9.

R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification”, IEEE Transactions on Systems, Man and Cybernetics (IEEE, 1973), pp. 610–621.

Y. Liu, K. Gadepalli, M. Norouzi, G. E. Dahl, T. Kohlberger, A. Boyko, S. Venugopalan, A. Timofeev, P. Q. Nelson, G. S. Corrado, J. D. Hipp, L. Peng, and M. C. Stumpe, “Detecting cancer metastases on gigapixel pathology images,” https://arxiv.org/abs/1703.02442 .

D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, “Deep learning for identifying metastatic breast cancer,” https://arxiv.org/abs/1606.05718 .

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Figures (7)

Fig. 1
Fig. 1 Imaging 10µm polystyrene spheres. (a) The original image obtained on the focus plane; (b) the retrieved 2-D phase map; (c) the reduced scattering coefficient μ S ' (µm−1); (d) the anisotropy factor g.
Fig. 2
Fig. 2 The architecture of the DCNN. The convolution layer conv1 outputs 96 images of size 126 × 126. The following convolutional layers output 128, 256, 256, 128 and 128 images, respectively. The full connection layers fc1 and fc2 output vectors of length 2048 and 1024, respectively. The full connection layer fc3 outputs the probability of being normal or cancer.
Fig. 3
Fig. 3 Normal lung tissue. (a) The original image obtained on the focus plane; (b) the retrieved 2-D phase map; (c) the gradient of phase map; (d) the scattering coefficient μ S (µm−1); (e) the reduced scattering coefficient μ S ' (µm−1); and (f) the anisotropy factor g.
Fig. 4
Fig. 4 Squamous cell lung cancer tissue. (a) The original image obtained on the focus plane; (b) the retrieved 2-D phase map; (c) the gradient of phase map; (d) the scattering coefficient μ S (µm−1); (e) the reduced scattering coefficient μ S ' (µm−1); and (f) the anisotropy factor g.
Fig. 5
Fig. 5 Histogram of the scattering coefficient μ S , the reduced scattering coefficient μ S ' , and the anisotropy factor g.
Fig. 6
Fig. 6 The train loss, the test loss, and the test accuracy of DCNN in training for the in-focus DIC images and the 2-D optical property maps, without or with data augmentation.
Fig 7
Fig 7 The Receiver Operating Characteristic curves of DCNN on the 2-D scattering parameter maps (Left) and the in-focus DIC images (Right) with and without data augmentation.

Tables (3)

Tables Icon

Table 1 The output and parameters of the DCNN.

Tables Icon

Table 2 The average scattering parameters of normal and cancerous lung tissue. Number inside parenthesis represents the standard deviation among measured cases.

Tables Icon

Table 3 Test accuracy and AUC of DCNN evaluated with the test data set.

Equations (5)

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

[I(x,y,z) φ(x,y,z)]=k I z
φ(x,y,z)= F 1 q (x,y) 2 F[ k lnI z ]
μ S L=2 1cosΔφ
μ S ' L= 1 2 k 2 | φ | 2
g=1 | φ | 2 4 k 2 1cosΔφ =1 μ S ' μ S

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