Burn-injured tissue detection for debridement surgery through the combination of non-invasive optical imaging techniques

Juan Heredia-Juesas, Jeffrey E. Thatcher, Yang Lu, John J. Squiers, Darlene King, Wensheng Fan, J. Michael Dimaio, Jose A. Martinez-Lorenzo

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue-healthy skin, viable wound bed, shallow burn, and deep burn-during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.

Original languageEnglish (US)
Article number#320874
Pages (from-to)1809-1826
Number of pages18
JournalBiomedical Optics Express
Volume9
Issue number4
DOIs
StatePublished - Apr 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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