Automated Extraction of Skin Wound Healing Biomarkers From In Vivo Label-Free Multiphoton Microscopy Using Convolutional Neural Networks.

Journal: Lasers in surgery and medicine
Published Date: Jan 13, 2021

Abstract

BACKGROUND AND OBJECTIVES: Histological analysis is a gold standard technique for studying impaired skin wound healing. Label-free multiphoton microscopy (MPM) can provide natural image contrast similar to histological sections and quantitative metabolic information using NADH and FAD autofluorescence. However, MPM analysis requires time-intensive manual segmentation of specific wound tissue regions limiting the practicality and usage of the technology for monitoring wounds. The goal of this study was to train a series of convolutional neural networks (CNNs) to segment MPM images of skin wounds to automate image processing and quantification of wound geometry and metabolism.

Authors

  • Jake D Jones
    Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas, USA.
  • Marcos R Rodriguez
    Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas.
  • Kyle P Quinn
    Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA; Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, 72701, USA.

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