2Siberian State Medical University, 634050 Tomsk, Russia
3Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Sciences, 634055 Tomsk, Russia
* To whom correspondence should be addressed.
Received August 3, 2018; Revised August 16, 2018; Accepted August 16, 2018
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.
KEY WORDS: collagen disorganization, multiphoton microscopy, second harmonic generation, autofluorescence, machine learning