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, 10 (7), 3353-3368
eCollection

Application of Multiphoton Imaging and Machine Learning to Lymphedema Tissue Analysis

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Application of Multiphoton Imaging and Machine Learning to Lymphedema Tissue Analysis

Yury V Kistenev et al. Biomed Opt Express.

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.

Conflict of interest statement

The authors declare that there are no conflicts of interest related to this article.

Figures

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.

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