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Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods

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Abstract

Pathogenesis of many diseases is associated with changes in the collagen spatial structure. Traditionally, the 3D structure of collagen in biological tissues is analyzed using histochemistry, immunohistochemistry, magnetic resonance imaging, and X-radiography. At present, multiphoton microscopy (MPM) is commonly used to study the structure of biological tissues. MPM has a high spatial resolution comparable to histological analysis and can be used for direct visualization of collagen spatial structure. Because of a large volume of data accumulated due to the high spatial resolution of MPM, special analytical methods should be used for identification of informative features in the images and quantitative evaluation of relationship between these features and pathological processes resulting in the destruction of collagen structure. Here, we describe current approaches and achievements in the identification of informative features in the MPM images of collagen in biological tissues, as well as the development on this basis of algorithms for computer-aided classification of collagen structures using machine learning as a type of artificial intelligence methods.

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Abbreviations

AGE:

advanced glycation end-product

ANN:

artificial neural network

AUC:

area under ROC curve

CNN:

convolutional neural network

CPA:

collagen proportional area

DTI:

diffusion tensor imaging

ECM:

extracellular matrix

FLIM:

fluorescence lifetime imaging

FOS:

first-order statistics

GLCM:

gray-level co-occurrence matrix

IR:

infrared

MP:

metalloproteinase

MPM:

multiphoton microscopy

MRI:

magnetic resonance imaging

PCA:

principal component analysis

ReLU:

rectified linear unit

ROC:

receiver operating characteristic

ROS:

reactive oxygen species

SHG:

second harmonic generation

SIFT:

scale invariant feature transform

SOS:

second-order statistics

SVM:

support vector machine

TCSPC:

time-correlated single photon counting

TDS:

training dataset

TPF:

two-photon fluorescence

TPM:

two-photon microscopy

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Authors and Affiliations

  1. Tomsk State University, 634050, Tomsk, Russia

    Yu. V. Kistenev, D. A. Vrazhnov, V. V. Nikolaev, E. A. Sandykova & N. A. Krivova

  2. Siberian State Medical University, 634050, Tomsk, Russia

    Yu. V. Kistenev, D. A. Vrazhnov, V. V. Nikolaev & E. A. Sandykova

  3. Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Sciences, 634055, Tomsk, Russia

    Yu. V. Kistenev

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  1. Yu. V. Kistenev
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  2. D. A. Vrazhnov
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  3. V. V. Nikolaev
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  4. E. A. Sandykova
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  5. N. A. Krivova
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Correspondence to Yu. V. Kistenev.

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Russian Text © Yu. V. Kistenev, D. A. Vrazhnov, V. V. Nikolaev, E. A. Sandykova, N. A. Krivova, 2019, published in Uspekhi Biologicheskoi Khimii, 2019, Vol. 59, pp. 219–252.

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Kistenev, Y.V., Vrazhnov, D.A., Nikolaev, V.V. et al. Analysis of Collagen Spatial Structure Using Multiphoton Microscopy and Machine Learning Methods. Biochemistry Moscow 84 (Suppl 1), 108–123 (2019). https://doi.org/10.1134/S0006297919140074

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  • DOI: https://doi.org/10.1134/S0006297919140074

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