4.6 Article

Quantification of scar collagen texture and prediction of scar development via second harmonic generation images and a generative adversarial network

Journal

BIOMEDICAL OPTICS EXPRESS
Volume 12, Issue 8, Pages 5305-5319

Publisher

OPTICAL SOC AMER
DOI: 10.1364/BOE.431096

Keywords

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Funding

  1. Special Funds of the Central Government Guiding Local Science and Technology Development [2020L3008]
  2. Fujian Provincial Health and Family Planning of Young and Middle Age Personnel Training Projects [2016ZQN27]
  3. United Fujian Provincial Health and Education Project for Tackling the Key Research of China [2019WJ03]
  4. Natural Science Foundation of Fujian Province [2019J01272]

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A novel method combining SHG imaging and deep learning algorithm is proposed for analyzing and diagnosing human scar texture. The model can accurately construct a regression model of scar texture and predict the age of the scar.
Widely used for medical analysis, the texture of the human scar tissue is characterized by irregular and extensive types. The quantitative detection and analysis of the scar texture as enabled by image analysis technology is of great significance to clinical practice. However, the existing methods remain disadvantaged by various shortcomings, such as the inability to fully extract the features of texture. Hence, the integration of second harmonic generation (SHG) imaging and deep learning algorithm is proposed in this study. Through combination with Tamura texture features, a regression model of the scar texture can be constructed to develop a novel method of computer-aided diagnosis, which can assist clinical diagnosis. Based on wavelet packet transform (WPT) and generative adversarial network (GAN), the model is trained with scar texture images of different ages. Generalized Boosted Regression Trees (GBRT) is also adopted to perform regression analysis. Then, the extracted features are further used to predict the age of scar. The experimental results obtained by our proposed model are better compared to the previously published methods. It thus contributes to the better understanding of the mechanism behind scar development and possibly the further development of SHG for skin analysis and clinic practice.

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