Application of multiphoton imaging and machine learning to lymphedema tissue analysis
SibMed researchers have presented the results of in-vivo two-photon imaging of lymphedema tissue. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers.
The research project aimed to observe the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, using various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning.
Researchers assert lymphedema diagnosis based on collagen disorganization analysis by multiphoton laser microscopy (MPM) and machine learning is very promising. Future research will aim to assess the threshold of sensitivity of this approach in the development of lymphedema.
The joint research has been undertaken by Siberian State Medical University, Tomsk State University, Institute of Strength Physics and Materials Science of Siberian Branch of the RAS, and Institute of Microsurgery.
Click the link below and read the full text of this paper published in Biomedical Optics Express.