Abstract

The results of in-vivo two-photon imaging of lymphedema tissue are presented. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers. The papillary layer of the skin with a penetration depth of about 100 μm was examined. Both the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, was observed. Various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning, were used. The classification by “ensemble learning” provided 96% accuracy in validating the data from the testing set.

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

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2019 (1)

Yu. V. Kistenev, D. A. Vrazhnov, V. V. Nikolaev, E. A. Sandykova, and N. A. Krivova, “Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods,” Biochemistry (Mosc.) 84(1), 108–123 (2019).
[Crossref]

2017 (4)

N. J. Jan, K. Lathrop, and I. A. Sigal, “Collagen architecture of the posterior pole: high-resolution wide field of view visualization and analysis using polarized light microscopy,” Invest. Ophthalmol. Vis. Sci. 58(2), 735–744 (2017).
[Crossref] [PubMed]

T. F. O’Donnell, J. C. Rasmussen, and E. M. Sevick-Muraca, “New diagnostic modalities in the evaluation of lymphedema,” J. Vasc. Surg. Venous Lymphat. Disord. 5(2), 261–273 (2017).
[Crossref] [PubMed]

M. R. Greives, M. B. Aldrich, E. M. Sevick-Muraca, and J. C. Rasmussen, “Near-infrared fluorescence lymphatic imaging of a toddler with congenital lymphedema,” Pediatrics 139(4), e20154456 (2017).
[Crossref] [PubMed]

E. A. Shirshin, Y. I. Gurfinkel, A. V. Priezzhev, V. V. Fadeev, J. Lademann, and M. E. Darvin, “Two-photon autofluorescence lifetime imaging of human skin papillary dermis in vivo: assessment of blood capillaries and structural proteins localization,” Sci. Rep. 7(1), 1171 (2017).
[Crossref] [PubMed]

2016 (2)

K. Rincon, P. Shah, J. Ramella-Roman, and S. Bhansali, “A review of engineering approaches for lymphedema detection,” IEEE Rev. Biomed. Eng. 9, 79–90 (2016).
[Crossref] [PubMed]

C. Oostendorp, P. J. E. Uijtdewilligen, E. M. Versteeg, T. G. Hafmans, E. H. van den Bogaard, P. K. J. D. de Jonge, A. Pirayesh, J. W. Von den Hoff, E. Reichmann, W. F. Daamen, and T. H. van Kuppevelt, “Visualisation of newly synthesised collagen in vitro and in vivo,” Sci. Rep. 6(1), 18780 (2016).
[Crossref] [PubMed]

2015 (3)

K. Tilbury and P. J. Campagnola, “Applications of Second-Harmonic Generation Imaging Microscopy in Ovarian And Breast Cancer,” Perspect. Medicin. Chem. 7, 21–32 (2015).
[Crossref] [PubMed]

O. J. Pallotta, M. van Zanten, M. McEwen, L. Burrow, J. Beesley, and N. Piller, “Development and validation of a custom made indocyanine green fluorescence lymphatic vessel imager,” J. Biomed. Opt. 20(6), 066003 (2015).
[Crossref] [PubMed]

Ş. Öztürk and B. Akdemir, “Comparison of edge detection algorithms for texture analysis on glass production,” Procedia Soc. Behav. Sci. 195, 2675–2682 (2015).
[Crossref]

2014 (1)

J.-C. Pittet, O. Freis, M.-D. Vazquez-Duchêne, G. Périé, and G. Pauly, “Evaluation of elastin/collagen content in human dermis in-vivo by multiphoton tomography – variation with depth and correlation with aging,” Cosmetics 1(3), 211–221 (2014).
[Crossref]

2013 (1)

Y. Dancik, A. Favre, C. J. Loy, A. V. Zvyagin, and M. S. Roberts, “Use of multiphoton tomography and fluorescence lifetime imaging to investigate skin pigmentation in vivo,” J. Biomed. Opt. 18(2), 026022 (2013).
[Crossref] [PubMed]

2012 (3)

M. Mihara, H. Hara, Y. Hayashi, M. Narushima, T. Yamamoto, T. Todokoro, T. Iida, N. Sawamoto, J. Araki, K. Kikuchi, N. Murai, T. Okitsu, I. Kisu, and I. Koshima, “Pathological steps of cancer-related lymphedema: histological changes in the collecting lymphatic vessels after lymphadenectomy,” PLoS One 7(7), e41126 (2012).
[Crossref] [PubMed]

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] [PubMed]

D. Belsare and M. M. Mushrif, “Histopathological image analysis using image processing techniques: An overview,” Signal & Image Processing: Int. J. 3(4), 23–26 (2012).

2011 (2)

K. Sugata, O. Osanai, T. Sano, and Y. Takema, “Evaluation of photoaging in facial skin by multiphoton laser scanning microscopy,” Skin Res. Technol. 17(1), 1–3 (2011).
[Crossref] [PubMed]

X. Wu, S. Zhuo, J. Chen, and N. Liu, “Real-time in vivo imaging collagen in lymphedematous skin using multiphoton microscopy,” Scanning 33(6), 463–467 (2011).
[Crossref] [PubMed]

2010 (1)

N. Unno, M. Nishiyama, M. Suzuki, H. Tanaka, N. Yamamoto, D. Sagara, Y. Mano, and H. Konno, “A novel method of measuring human lymphatic pumping using indocyanine green fluorescence lymphography,” J. Vasc. Surg. 52(4), 946–952 (2010).
[Crossref] [PubMed]

2009 (5)

J. C. Rasmussen, I.-C. Tan, M. V. Marshall, C. E. Fife, and E. M. Sevick-Muraca, “Lymphatic imaging in humans with near-infrared fluorescence,” Curr. Opin. Biotechnol. 20(1), 74–82 (2009).
[Crossref] [PubMed]

H. Brorson, K. Ohlin, G. Olsson, and M. K. Karlsson, “Breast cancer-related chronic arm lymphedema is associated with excess adipose and muscle tissue,” Lymphat. Res. Biol. 7(1), 3–10 (2009).
[Crossref] [PubMed]

International Society of Lymphology, “The diagnosis and treatment of peripheral lymphedema. 2009 Concensus Document of the International Society of Lymphology,” Lymphology 42(2), 51–60 (2009).
[PubMed]

M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological Image Analysis: A Review,” IEEE Rev. Biomed. Eng. 2, 147–171 (2009).
[Crossref] [PubMed]

R. Maini and H. Aggarwal, “Study and comparison of various image edge detection techniques,” International journal of image processing 3(1), 1–11 (2009).

2008 (1)

K. König, “Clinical multiphoton tomography,” J. Biophotonics 1(1), 13–23 (2008).
[Crossref] [PubMed]

2007 (1)

A. G. Warren, H. Brorson, L. J. Borud, and S. A. Slavin, “Lymphedema: a comprehensive review,” Ann. Plast. Surg. 59(4), 464–472 (2007).
[Crossref] [PubMed]

2006 (2)

G. Topping, J. Malda, R. Dawson, and Z. Upton, “Development and characterization of human skin equivalents and their potential application as a burn wound model,” Primary Intention. 14, 14–21 (2006).

R. Tabibiazar, L. Cheung, J. Han, J. Swanson, A. Beilhack, A. An, S. S. Dadras, N. Rockson, S. Joshi, R. Wagner, and S. G. Rockson, “Inflammatory Manifestations of Experimental Lymphatic Insufficiency,” PLoS Med. 3(7), e254 (2006).
[Crossref] [PubMed]

2005 (2)

V. Vapnik, “Universal learning technology: Support vector machines,” NEC Journal of Advanced Technology 2(2), 137–144 (2005).

H. M. Harb, A. S. Desuky, A. Mohammed, and R. Jennane, “Histogram of Oriented Gradients and Texture Features for Bone Texture Characterization,” Int. J. Comput. Appl. 975, 8887 (2005).

2004 (1)

T. Yasui, Y. Tohno, and T. Araki, “Characterization of collagen orientation in human dermis by two-dimensional second-harmonic-generation polarimetry,” J. Biomed. Opt. 9(2), 259–264 (2004).
[Crossref] [PubMed]

2002 (1)

A. Liaw and M. Wiener, “Classification and regression by random forest,” R News 2(3), 18–22 (2002).

2001 (1)

J. Malik, S. Belongie, T. Leung, and J. Shi, “Contour and Texture Analysis for Image Segmentation,” Int. J. Comput. Vis. 43(1), 7–27 (2001).
[Crossref]

1998 (3)

R. Kohavi and F. Provost, “Glossary of terms,” Mach. Learn. 30(2–3), 271–274 (1998).

H. Brorson, H. Svensson, K. Norrgren, and O. Thorsson, “Liposuction reduces arm lymphedema without significantly altering the already impaired lymph transport,” Lymphology 31(4), 156–172 (1998).
[PubMed]

T. Lindeberg, “Edge detection and ridge detection with automatic scale selection,” Int. J. Comput. Vis. 30(2), 117–156 (1998).
[Crossref]

1996 (1)

L. Breiman, “Bagging predictors,” Mach. Learn. 24(2), 123–140 (1996).
[Crossref]

1993 (2)

J. F. Rivest, P. Soille, and S. Beucher, “Morphological gradients,” J. Electron. Imaging 2(4), 326–336 (1993).
[Crossref]

R. A. Cambria, P. Gloviczki, J. M. Naessens, and H. W. Wahner, “Noninvasive evaluation of the lymphatic system with lymphoscintigraphy: a prospective, semiquantitative analysis in 386 extremities,” J. Vasc. Surg. 18(5), 773–782 (1993).
[Crossref] [PubMed]

1988 (1)

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-State Circuits 23(2), 358–367 (1988).
[Crossref]

1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986).
[Crossref] [PubMed]

1985 (1)

N. S. Hadjis, D. H. Carr, L. Banks, and J. J. Pflug, “The role of CT in the diagnosis of primary lymphedema of the lower limb,” AJR Am. J. Roentgenol. 144(2), 361–364 (1985).
[Crossref] [PubMed]

1981 (1)

D. E. Drehmer and G. W. Morris, “Cross-Validation with small samples: An algorithm for computing Gollob’s estimator,” Educ. Psychol. Meas. 41(1), 195–200 (1981).
[Crossref]

1970 (1)

J. M. Prewitt, “Object enhancement and extraction,” Picture processing and Psychopictorics 10(1), 15–19 (1970).

1953 (1)

A. I. Sherman and M. Ter-Pogossian, “Lymph-node concentration of radioactive colloidal gold following interstitial injection,” Cancer 6(6), 1238–1240 (1953).
[Crossref] [PubMed]

1952 (1)

J. Kinmonth, “Lymphoangiography in man: a method of outlining lymphatic trunks at operation,” J. Clin. Sci. 11(1), 13 (1952).

Aggarwal, H.

R. Maini and H. Aggarwal, “Study and comparison of various image edge detection techniques,” International journal of image processing 3(1), 1–11 (2009).

Akdemir, B.

Ş. Öztürk and B. Akdemir, “Comparison of edge detection algorithms for texture analysis on glass production,” Procedia Soc. Behav. Sci. 195, 2675–2682 (2015).
[Crossref]

Aldrich, M. B.

M. R. Greives, M. B. Aldrich, E. M. Sevick-Muraca, and J. C. Rasmussen, “Near-infrared fluorescence lymphatic imaging of a toddler with congenital lymphedema,” Pediatrics 139(4), e20154456 (2017).
[Crossref] [PubMed]

An, A.

R. Tabibiazar, L. Cheung, J. Han, J. Swanson, A. Beilhack, A. An, S. S. Dadras, N. Rockson, S. Joshi, R. Wagner, and S. G. Rockson, “Inflammatory Manifestations of Experimental Lymphatic Insufficiency,” PLoS Med. 3(7), e254 (2006).
[Crossref] [PubMed]

Araki, J.

M. Mihara, H. Hara, Y. Hayashi, M. Narushima, T. Yamamoto, T. Todokoro, T. Iida, N. Sawamoto, J. Araki, K. Kikuchi, N. Murai, T. Okitsu, I. Kisu, and I. Koshima, “Pathological steps of cancer-related lymphedema: histological changes in the collecting lymphatic vessels after lymphadenectomy,” PLoS One 7(7), e41126 (2012).
[Crossref] [PubMed]

Araki, T.

T. Yasui, Y. Tohno, and T. Araki, “Characterization of collagen orientation in human dermis by two-dimensional second-harmonic-generation polarimetry,” J. Biomed. Opt. 9(2), 259–264 (2004).
[Crossref] [PubMed]

Baker, R. L.

N. Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE J. Solid-State Circuits 23(2), 358–367 (1988).
[Crossref]

Banks, L.

N. S. Hadjis, D. H. Carr, L. Banks, and J. J. Pflug, “The role of CT in the diagnosis of primary lymphedema of the lower limb,” AJR Am. J. Roentgenol. 144(2), 361–364 (1985).
[Crossref] [PubMed]

Beesley, J.

O. J. Pallotta, M. van Zanten, M. McEwen, L. Burrow, J. Beesley, and N. Piller, “Development and validation of a custom made indocyanine green fluorescence lymphatic vessel imager,” J. Biomed. Opt. 20(6), 066003 (2015).
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Beilhack, A.

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Figures (11)

Fig. 1
Fig. 1 Association between the NECST and the lymphedema stage classification [21]. The horizontal axis shows the stages of lymphedema. The vertical axis shows the percentage of the NECST in specimens sampled at each stage of the disease.
Fig. 2
Fig. 2 Photo of a patient with II stage lymphedema after surgical treatment of breast cancer.
Fig. 3
Fig. 3 Block-scheme of the MPM device. Here, the PMT is a photomultiplier tube.
Fig. 4
Fig. 4 SHG image of healthy volunteers (a) and patients with II stage of lymphedema (b).
Fig. 5
Fig. 5 The results of the edge detection procedure, applied to lymphedema tissue (the upper row) and healthy tissue (the lower row); initial images (a, f), results of initial image filtering by: (b, g) - the Sobel operator, (c, h) - the Canny edge detector, (d, i) - the morphology method, (e, j) – Laplacian of Gaussian.
Fig. 6
Fig. 6 The total edge length on the images of healthy and lymphedema tissue, calculated using the Sobel operator, the Canny edge detector, the morphology method, and the LoG operator.
Fig. 7
Fig. 7 The image is divided into 32 × 32 pixel patches, each is divided into 8 × 8 pixel cells that are combined into a 24 × 24 pixel block.
Fig. 8
Fig. 8 Example of model data: (a) – organized fibers, positive samples, (b) – disorganized fibers, negative samples.
Fig. 9
Fig. 9 Analysis of the optimal discretization level of the brightness gradient orientations and amplitudes together (a) and separately (b, d), and optimal block size (c).
Fig. 10
Fig. 10 Selection of significant cells on an SHG tissue image
Fig. 11
Fig. 11 Scheme of SHG image classification.

Tables (2)

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Table 1 The estimation of collagen and elastin content in the tissues, Me [Q25; Q75].

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Table 2 Classification accuracy for the TEL feature on the images of healthy and lymphedema tissue.

Equations (5)

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

G= G x 2 + G y 2 ; G x =[ +1 0 1 +2 0 2 +1 0 1 ]; G y =[ +1 +2 +1 0 0 0 1 2 1 ],
G x =[ +1 0 1 +1 0 1 +1 0 1 ]; G y =[ +1 +1 +1 0 0 0 1 1 1 ].
[ +1 +1 +1 +1 8 +1 +1 1 +1 ];[ 1 +2 1 +2 4 +2 1 +2 1 ].
Sensitivity=TP/(TP+FN);Specificity=TN/(TN+FP);
Accuracy=(TP+TN)/(TP+TN+FP+FN),

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