Open AccessApplication of multiphoton imaging and machine learning to lymphedema tissue analysis
Author(s) -
Yury V. Kistenev,
Viktor V. Nikolaev,
O. S. Kurochkina,
А. В. Борисов,
Denis A. Vrazhnov,
Ekaterina A. Sandykova
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.003353
Subject(s) - lymphedema , medicine , fibrosis , biomedical engineering , pathology , nuclear medicine , cancer , breast cancer
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.