Abstract

Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained fixed tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained convolutional neural networks. Our results suggest that deep learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.

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

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2019 (4)

N. Borhani, A. J. Bower, S. A. Boppart, and D. Psaltis, “Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy,” Biomed. Opt. Express 10(3), 1339–1350 (2019).
[Crossref]

Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, H. Günaydın, J. E. Zuckerman, T. Chong, and A. E. Sisk, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
[Crossref]

A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” N. Engl. J. Med. 380(14), 1347–1358 (2019).
[Crossref]

H. Lin, C. Wei, G. Wang, H. Chen, L. Lin, M. Ni, J. Chen, and S. Zhuo, “Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning,” J. Biophotonics 12(7), e201800435 (2019).
[Crossref]

2018 (6)

Y. Yu, J. Wang, C. W. Ng, Y. Ma, S. Mo, E. L. S. Fong, J. Xing, Z. Song, Y. Xie, and K. Si, “Deep learning enables automated scoring of liver fibrosis stages,” Sci. Rep. 8(1), 16016 (2018).
[Crossref]

M. Wainberg, D. Merico, A. Delong, and B. J. Frey, “Deep learning in biomedicine,” Nat. Biotechnol. 36(9), 829–838 (2018).
[Crossref]

G. Hinton, “Deep learning—a technology with the potential to transform health care,” JAMA 320(11), 1101–1102 (2018).
[Crossref]

M. J. Huttunen, A. Hassan, C. W. McCloskey, S. Fasih, J. Upham, B. C. Vanderhyden, R. W. Boyd, and S. Murugkar, “Automated classification of multiphoton microscopy images of ovarian tissue using deep learning,” J. Biomed. Opt. 23(06), 1 (2018).
[Crossref]

H. A. Haenssle, C. Fink, R. Schneiderbauer, F. Toberer, T. Buhl, A. Blum, A. Kalloo, A. B. H. Hassen, L. Thomas, and A. Enk, “Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists,” Ann. Oncol. 29(8), 1836–1842 (2018).
[Crossref]

H. G. Breunig, B. Sauer, A. Batista, and K. König, “Rapid vertical tissue imaging with clinical multiphoton tomography,” Proc. SPIE 10679, 81 (2018).
[Crossref]

2017 (6)

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref]

O. J. Muensterer, S. Waldron, Y. J. Boo, C. Ries, L. Sehls, F. Simon, L. Seidmann, J. Birkenstock, and J. Gödeke, “Multiphoton microscopy: a novel diagnostic method for solid tumors in a prospective pediatric oncologic cohort, an experimental study,” Int. J. Surg. 48, 128–133 (2017).
[Crossref]

T. Y. Sun, A. M. Haberman, and V. Greco, “Preclinical advances with multiphoton microscopy in live imaging of skin cancers,” J. Invest. Dermatol. 137(2), 282–287 (2017).
[Crossref]

S. G. Stanciu, F. J. Ávila, R. Hristu, and J. M. Bueno, “A study on image quality in polarization-resolved second harmonic generation microscopy,” Sci. Rep. 7(1), 15476 (2017).
[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM 60(2), 84–90 (2017).
[Crossref]

J. Chapin, J. Bamme, F. Hsu, P. Christos, and M. DeSancho, “Outcomes in patients with hemophilia and von Willebrand disease undergoing invasive or surgical procedures,” Clin. Appl. Thromb./Hemostasis 23(2), 148–154 (2017).
[Crossref]

2016 (1)

T. W. Bocklitz, F. S. Salah, N. Vogler, S. Heuke, O. Chernavskaia, C. Schmidt, M. J. Waldner, F. R. Greten, R. Bräuer, and M. Schmitt, “Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool,” BMC cancer 16(1), 534 (2016).
[Crossref]

2015 (6)

R. B. Saager, M. Balu, V. Crosignani, A. Sharif, A. J. Durkin, K. M. Kelly, and B. J. Tromberg, “In vivo measurements of cutaneous melanin across spatial scales: using multiphoton microscopy and spatial frequency domain spectroscopy,” J. Biomed. Opt. 20(6), 066005 (2015).
[Crossref]

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

M. Balu, C. B. Zachary, R. M. Harris, T. B. Krasieva, K. König, B. J. Tromberg, and K. M. Kelly, “In vivo multiphoton microscopy of basal cell carcinoma,” JAMA Dermatol. 151(10), 1068–1074 (2015).
[Crossref]

S. G. Stanciu, S. Xu, Q. Peng, J. Yan, G. A. Stanciu, R. E. Welsch, P. T. So, G. Csucs, and H. Yu, “Experimenting liver fibrosis diagnostic by two photon excitation microscopy and bag-of-features image classification,” Sci. Rep. 4(1), 4636 (2015).
[Crossref]

K. Abhishek and N. Khunger, “Complications of skin biopsy,” J Cutan Aesthet Surg 8(4), 239 (2015).
[Crossref]

Q. Sun, W. Zheng, J. Wang, Y. Luo, and J. Y. Qu, “Mechanism of two-photon excited hemoglobin fluorescence emission,” J. Biomed. Opt. 20(10), 105014 (2015).
[Crossref]

2014 (4)

M. Weinigel, H. Breunig, M. Kellner-Höfer, R. Bückle, M. Darvin, M. Klemp, J. Lademann, and K. König, “In vivo histology: optical biopsies with chemical contrast using clinical multiphoton/coherent anti-Stokes Raman scattering tomography,” Laser Phys. Lett. 11(5), 055601 (2014).
[Crossref]

C. Bonnans, J. Chou, and Z. Werb, “Remodelling the extracellular matrix in development and disease,” Nat. Rev. Mol. Cell Biol. 15(12), 786–801 (2014).
[Crossref]

R. Cicchi, D. Kapsokalyvas, and F. S. Pavone, “Clinical nonlinear laser imaging of human skin: a review,” BioMed Res. Int. 2014, 1–14 (2014).
[Crossref]

S. Chatterjee, “Artefacts in histopathology,” Int. J. Oral Maxillofac. Pathol. 18(4), 111 (2014).
[Crossref]

2013 (2)

R. K. Benninger and D. W. Piston, “Two-photon excitation microscopy for the study of living cells and tissues,” Curr. Protoc. Cell Biol. 59(1), 1–24 (2013).
[Crossref]

E. E. Hoover and J. A. Squier, “Advances in multiphoton microscopy technology,” Nat. Photonics 7(2), 93–101 (2013).
[Crossref]

2012 (4)

P. Lu, V. M. Weaver, and Z. Werb, “The extracellular matrix: a dynamic niche in cancer progression,” J. Cell Biol. 196(4), 395–406 (2012).
[Crossref]

X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012).
[Crossref]

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, and B. Schmid, “Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9(7), 676–682 (2012).
[Crossref]

I. Georgakoudi and K. P. Quinn, “Optical imaging using endogenous contrast to assess metabolic state,” Annu. Rev. Biomed. Eng. 14(1), 351–367 (2012).
[Crossref]

2011 (3)

W. Zheng, D. Li, Y. Zeng, Y. Luo, and J. Y. Qu, “Two-photon excited hemoglobin fluorescence,” Biomed. Opt. Express 2(1), 71–79 (2011).
[Crossref]

M. J. Koehler, M. Speicher, S. Lange-Asschenfeldt, E. Stockfleth, S. Metz, P. Elsner, M. Kaatz, and K. König, “Clinical application of multiphoton tomography in combination with confocal laser scanning microscopy for in vivo evaluation of skin diseases,” Exp. Dermatol. 20(7), 589–594 (2011).
[Crossref]

P. J. Campagnola and C. Y. Dong, “Second harmonic generation microscopy: principles and applications to disease diagnosis,” Laser Photonics Rev. 5(1), 13–26 (2011).
[Crossref]

2010 (1)

D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” J Mach. Learn. Res. 11, 625–660 (2010).

2009 (2)

E. Dimitrow, M. Ziemer, M. J. Koehler, J. Norgauer, K. König, P. Elsner, and M. Kaatz, “Sensitivity and specificity of multiphoton laser tomography for in vivo and ex vivo diagnosis of malignant melanoma,” J. Invest. Dermatol. 129(7), 1752–1758 (2009).
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J. Paoli, M. Smedh, and M. B. Ericson, “Multiphoton laser scanning microscopy–a novel diagnostic method for superficial skin cancers,” Semin. Cutaneous Med. Surg. 28(3), 190–195 (2009).
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2008 (2)

J. Paoli, M. Smedh, A.-M. Wennberg, and M. B. Ericson, “Multiphoton laser scanning microscopy on non-melanoma skin cancer: morphologic features for future non-invasive diagnostics,” J. Invest. Dermatol. 128(5), 1248–1255 (2008).
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K. König, “Clinical multiphoton tomography,” J. Biophotonics 1(1), 13–23 (2008).
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2007 (1)

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. 104(49), 19494–19499 (2007).
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2006 (2)

L. Gao, L. Jin, P. Xue, J. Xu, Y. Wang, H. Ma, and D. Chen, “Reconstruction of complementary images in second harmonic generation microscopy,” Opt. Express 14(11), 4727–4735 (2006).
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T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44(3), 145–152 (2006).
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2005 (1)

M. C. Skala, J. M. Squirrell, K. M. Vrotsos, J. C. Eickhoff, A. Gendron-Fitzpatrick, K. W. Eliceiri, and N. Ramanujam, “Multiphoton microscopy of endogenous fluorescence differentiates normal, precancerous, and cancerous squamous epithelial tissues,” Cancer Res. 65(4), 1180–1186 (2005).
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2004 (1)

L. Brown, “Improving histopathology turnaround time: a process management approach,” Curr. Diagn. Pathol. 10(6), 444–452 (2004).
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2003 (4)

P. J. Campagnola and L. M. Loew, “Second-harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms,” Nat. Biotechnol. 21(11), 1356–1360 (2003).
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W. R. Zipfel, R. M. Williams, R. Christie, A. Y. Nikitin, B. T. Hyman, and W. W. Webb, “Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation,” Proc. Natl. Acad. Sci. 100(12), 7075–7080 (2003).
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W. R. Zipfel, R. M. Williams, and W. W. Webb, “Nonlinear magic: multiphoton microscopy in the biosciences,” Nat. Biotechnol. 21(11), 1369–1377 (2003).
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A. Bayat, D. McGrouther, and M. Ferguson, “Skin scarring,” BMJ 326(7380), 88–92 (2003).
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2001 (3)

M. Huzaira, F. Rius, M. Rajadhyaksha, R. R. Anderson, and S. González, “Topographic variations in normal skin, as viewed by in vivo reflectance confocal microscopy,” J. Invest. Dermatol. 116(6), 846–852 (2001).
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R. M. Williams, W. R. Zipfel, and W. W. Webb, “Multiphoton microscopy in biological research,” Curr. Opin. Chem. Biol. 5(5), 603–608 (2001).
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E. W. Raines, “The extracellular matrix can regulate vascular cell migration, proliferation, and survival: relationships to vascular disease,” Int. J. Exp. Pathol. 81(3), 173–182 (2001).
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2000 (1)

P. T. So, C. Y. Dong, B. R. Masters, and K. M. Berland, “Two-photon excitation fluorescence microscopy,” Annu. Rev. Biomed. Eng. 2(1), 399–429 (2000).
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1997 (1)

G. Van Kempen, L. Van Vliet, P. Verveer, and H. Van Der Voort, “A quantitative comparison of image restoration methods for confocal microscopy,” J. Microsc. 185(3), 354–365 (1997).
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1992 (1)

L. M. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever, “Scale and the differential structure of images,” Image Vision Comput. 10(6), 376–388 (1992).
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1991 (1)

M. Branchet, S. Boisnic, C. Frances, C. Lesty, and L. Robert, “Morphometric analysis of dermal collagen fibers in normal human skin as a function of age,” Arch. Gerontol. Geriatr. 13(1), 1–14 (1991).
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1988 (1)

B. J. Reid, R. C. Haggitt, C. E. Rubin, G. Roth, C. M. Surawicz, G. Vanbelle, K. Lewin, W. M. Weinstein, D. A. Antonioli, H. Goldman, W. Macdonald, and D. Owen, “Observer Variation in the Diagnosis of Dysplasia in Barretts Esophagus,” Hum. Pathol. 19(2), 166–178 (1988).
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1984 (1)

E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA engineer 29, 33–41 (1984).

1983 (1)

S. Barsky, G. Siegal, F. Jannotta, and L. Liotta, “Loss of basement membrane components by invasive tumors but not by their benign counterparts,” Lab. Invest.; a journal of technical methods and pathology 49, 140–147 (1983).

1975 (1)

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Anderson, R. R.

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Antonioli, D. A.

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J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, and B. Schmid, “Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9(7), 676–682 (2012).
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Ávila, F. J.

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Balu, M.

R. B. Saager, M. Balu, V. Crosignani, A. Sharif, A. J. Durkin, K. M. Kelly, and B. J. Tromberg, “In vivo measurements of cutaneous melanin across spatial scales: using multiphoton microscopy and spatial frequency domain spectroscopy,” J. Biomed. Opt. 20(6), 066005 (2015).
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S. Barsky, G. Siegal, F. Jannotta, and L. Liotta, “Loss of basement membrane components by invasive tumors but not by their benign counterparts,” Lab. Invest.; a journal of technical methods and pathology 49, 140–147 (1983).

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H. G. Breunig, B. Sauer, A. Batista, and K. König, “Rapid vertical tissue imaging with clinical multiphoton tomography,” Proc. SPIE 10679, 81 (2018).
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A. Bayat, D. McGrouther, and M. Ferguson, “Skin scarring,” BMJ 326(7380), 88–92 (2003).
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D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” J Mach. Learn. Res. 11, 625–660 (2010).

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E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA engineer 29, 33–41 (1984).

Berland, K. M.

P. T. So, C. Y. Dong, B. R. Masters, and K. M. Berland, “Two-photon excitation fluorescence microscopy,” Annu. Rev. Biomed. Eng. 2(1), 399–429 (2000).
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T. W. Bocklitz, F. S. Salah, N. Vogler, S. Heuke, O. Chernavskaia, C. Schmidt, M. J. Waldner, F. R. Greten, R. Bräuer, and M. Schmitt, “Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool,” BMC cancer 16(1), 534 (2016).
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M. Branchet, S. Boisnic, C. Frances, C. Lesty, and L. Robert, “Morphometric analysis of dermal collagen fibers in normal human skin as a function of age,” Arch. Gerontol. Geriatr. 13(1), 1–14 (1991).
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C. Bonnans, J. Chou, and Z. Werb, “Remodelling the extracellular matrix in development and disease,” Nat. Rev. Mol. Cell Biol. 15(12), 786–801 (2014).
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O. J. Muensterer, S. Waldron, Y. J. Boo, C. Ries, L. Sehls, F. Simon, L. Seidmann, J. Birkenstock, and J. Gödeke, “Multiphoton microscopy: a novel diagnostic method for solid tumors in a prospective pediatric oncologic cohort, an experimental study,” Int. J. Surg. 48, 128–133 (2017).
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M. Branchet, S. Boisnic, C. Frances, C. Lesty, and L. Robert, “Morphometric analysis of dermal collagen fibers in normal human skin as a function of age,” Arch. Gerontol. Geriatr. 13(1), 1–14 (1991).
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T. W. Bocklitz, F. S. Salah, N. Vogler, S. Heuke, O. Chernavskaia, C. Schmidt, M. J. Waldner, F. R. Greten, R. Bräuer, and M. Schmitt, “Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool,” BMC cancer 16(1), 534 (2016).
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Breunig, H.

M. Weinigel, H. Breunig, M. Kellner-Höfer, R. Bückle, M. Darvin, M. Klemp, J. Lademann, and K. König, “In vivo histology: optical biopsies with chemical contrast using clinical multiphoton/coherent anti-Stokes Raman scattering tomography,” Laser Phys. Lett. 11(5), 055601 (2014).
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Breunig, H. G.

H. G. Breunig, B. Sauer, A. Batista, and K. König, “Rapid vertical tissue imaging with clinical multiphoton tomography,” Proc. SPIE 10679, 81 (2018).
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Briggaman, R. A.

R. A. Briggaman and C. E. Wheeler, “The epidermal-dermal junction,” J. Invest. Dermatol. 65(1), 71–84 (1975).
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L. Brown, “Improving histopathology turnaround time: a process management approach,” Curr. Diagn. Pathol. 10(6), 444–452 (2004).
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Bückle, R.

M. Weinigel, H. Breunig, M. Kellner-Höfer, R. Bückle, M. Darvin, M. Klemp, J. Lademann, and K. König, “In vivo histology: optical biopsies with chemical contrast using clinical multiphoton/coherent anti-Stokes Raman scattering tomography,” Laser Phys. Lett. 11(5), 055601 (2014).
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S. G. Stanciu, F. J. Ávila, R. Hristu, and J. M. Bueno, “A study on image quality in polarization-resolved second harmonic generation microscopy,” Sci. Rep. 7(1), 15476 (2017).
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H. A. Haenssle, C. Fink, R. Schneiderbauer, F. Toberer, T. Buhl, A. Blum, A. Kalloo, A. B. H. Hassen, L. Thomas, and A. Enk, “Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists,” Ann. Oncol. 29(8), 1836–1842 (2018).
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Burt, P. J.

E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA engineer 29, 33–41 (1984).

Campagnola, P. J.

X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012).
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P. J. Campagnola and C. Y. Dong, “Second harmonic generation microscopy: principles and applications to disease diagnosis,” Laser Photonics Rev. 5(1), 13–26 (2011).
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P. J. Campagnola and L. M. Loew, “Second-harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms,” Nat. Biotechnol. 21(11), 1356–1360 (2003).
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Chapin, J.

J. Chapin, J. Bamme, F. Hsu, P. Christos, and M. DeSancho, “Outcomes in patients with hemophilia and von Willebrand disease undergoing invasive or surgical procedures,” Clin. Appl. Thromb./Hemostasis 23(2), 148–154 (2017).
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H. Lin, C. Wei, G. Wang, H. Chen, L. Lin, M. Ni, J. Chen, and S. Zhuo, “Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning,” J. Biophotonics 12(7), e201800435 (2019).
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X. Chen, O. Nadiarynkh, S. Plotnikov, and P. J. Campagnola, “Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure,” Nat. Protoc. 7(4), 654–669 (2012).
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Chernavskaia, O.

T. W. Bocklitz, F. S. Salah, N. Vogler, S. Heuke, O. Chernavskaia, C. Schmidt, M. J. Waldner, F. R. Greten, R. Bräuer, and M. Schmitt, “Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool,” BMC cancer 16(1), 534 (2016).
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Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, H. Günaydın, J. E. Zuckerman, T. Chong, and A. E. Sisk, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
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Chou, J.

C. Bonnans, J. Chou, and Z. Werb, “Remodelling the extracellular matrix in development and disease,” Nat. Rev. Mol. Cell Biol. 15(12), 786–801 (2014).
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W. R. Zipfel, R. M. Williams, R. Christie, A. Y. Nikitin, B. T. Hyman, and W. W. Webb, “Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation,” Proc. Natl. Acad. Sci. 100(12), 7075–7080 (2003).
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J. Chapin, J. Bamme, F. Hsu, P. Christos, and M. DeSancho, “Outcomes in patients with hemophilia and von Willebrand disease undergoing invasive or surgical procedures,” Clin. Appl. Thromb./Hemostasis 23(2), 148–154 (2017).
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D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” J Mach. Learn. Res. 11, 625–660 (2010).

Crosignani, V.

R. B. Saager, M. Balu, V. Crosignani, A. Sharif, A. J. Durkin, K. M. Kelly, and B. J. Tromberg, “In vivo measurements of cutaneous melanin across spatial scales: using multiphoton microscopy and spatial frequency domain spectroscopy,” J. Biomed. Opt. 20(6), 066005 (2015).
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Y. Rivenson, H. Wang, Z. Wei, K. de Haan, Y. Zhang, Y. Wu, H. Günaydın, J. E. Zuckerman, T. Chong, and A. E. Sisk, “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning,” Nat. Biomed. Eng. 3(6), 466–477 (2019).
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DeSancho, M.

J. Chapin, J. Bamme, F. Hsu, P. Christos, and M. DeSancho, “Outcomes in patients with hemophilia and von Willebrand disease undergoing invasive or surgical procedures,” Clin. Appl. Thromb./Hemostasis 23(2), 148–154 (2017).
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E. Dimitrow, M. Ziemer, M. J. Koehler, J. Norgauer, K. König, P. Elsner, and M. Kaatz, “Sensitivity and specificity of multiphoton laser tomography for in vivo and ex vivo diagnosis of malignant melanoma,” J. Invest. Dermatol. 129(7), 1752–1758 (2009).
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Dong, C. Y.

P. J. Campagnola and C. Y. Dong, “Second harmonic generation microscopy: principles and applications to disease diagnosis,” Laser Photonics Rev. 5(1), 13–26 (2011).
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P. T. So, C. Y. Dong, B. R. Masters, and K. M. Berland, “Two-photon excitation fluorescence microscopy,” Annu. Rev. Biomed. Eng. 2(1), 399–429 (2000).
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition, (Ieee, 2009), 248–255.

Dumitru, A.

M. J. Huttunen, R. Hristu, A. Dumitru, M. Costache, and S. G. Stanciu, “Investigating and Assessing the Dermoepidermal Junction with Multiphoton Microscopy and Deep Learning,” bioRxiv, 743054 (2019).

Durkin, A. J.

R. B. Saager, M. Balu, V. Crosignani, A. Sharif, A. J. Durkin, K. M. Kelly, and B. J. Tromberg, “In vivo measurements of cutaneous melanin across spatial scales: using multiphoton microscopy and spatial frequency domain spectroscopy,” J. Biomed. Opt. 20(6), 066005 (2015).
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M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. 104(49), 19494–19499 (2007).
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M. C. Skala, J. M. Squirrell, K. M. Vrotsos, J. C. Eickhoff, A. Gendron-Fitzpatrick, K. W. Eliceiri, and N. Ramanujam, “Multiphoton microscopy of endogenous fluorescence differentiates normal, precancerous, and cancerous squamous epithelial tissues,” Cancer Res. 65(4), 1180–1186 (2005).
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M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. 104(49), 19494–19499 (2007).
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M. C. Skala, J. M. Squirrell, K. M. Vrotsos, J. C. Eickhoff, A. Gendron-Fitzpatrick, K. W. Eliceiri, and N. Ramanujam, “Multiphoton microscopy of endogenous fluorescence differentiates normal, precancerous, and cancerous squamous epithelial tissues,” Cancer Res. 65(4), 1180–1186 (2005).
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Figures (6)

Fig. 1.
Fig. 1. Configuration of the MPM imaging setup.
Fig. 2.
Fig. 2. Convolutional Neural Network based workflow for binary classification of MPM images. The input images are fed to the pre-trained network (GoogLeNet) that first performs feature extraction effectively transforming the data into a more optimal representation. Subsequent image classification is performed by the FC, Softmax and classification layers that, contrary to the convolutional layers, are trained from scratch during the fine-training process.
Fig. 3.
Fig. 3. Schematic demonstration of the employed imaging protocol. (a) Photographs of two histology slides (75 × 25 mm) containing tissue fragments consecutively cut from a histological block, (left) stained with H&E, and (right) unstained. (b) Large mosaic depicting the entire histology slide assembled by stitching overlapping BM images collected at low magnification (5X obj.) (c) Example of MPM images (40X obj.) collected on the unstained samples at random positions across the DEJ. All acquired MPM images were registered to high magnification BM images (50X obj.) collected on the corresponding regions of the H&E stained sample, in order to re-confirm that they indeed depict the DEJ, which is captured transversally. Scale bar in SHG/TPEF images: 50 µm.
Fig. 4.
Fig. 4. Pairs of MPM and BM images of the DEJ collected on unstained (MPM) and H&E stained (BM) normal and dysplastic epithelial tissues. MPM Field of view: 250 × 250 µm2.
Fig. 5.
Fig. 5. Pairs of MPM and BM images of upper tumor borders collected on unstained (MPM) and H&E stained (BM) malignant epithelial tissues. MPM Field of view: 250 × 250 µm2.
Fig. 6.
Fig. 6. Calculated classification sensitivity (a), specificity (b) and accuracy (c) with the error bars corresponding to the respective standard deviations. (a)-(c) MPM Image classification performance is on average improved ∼4% when the data augmentation includes also a 5-level Gaussian pyramid (red markers), compared to data augmentation not utilizing the Gaussian pyramid (blue markers). (a)–(c) Highest MPM image classification sensitivity (93.5 ± 2.3%), specificity (95.0 ± 2.4%) and accuracy (94.2 ± 1.6%) are achieved by using combined TPEF + SHG images for training/validation.

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