TY - JOUR
T1 - Burn-injured tissue detection for debridement surgery through the combination of non-invasive optical imaging techniques
AU - Heredia-Juesas, Juan
AU - Thatcher, Jeffrey E.
AU - Lu, Yang
AU - Squiers, John J.
AU - King, Darlene
AU - Fan, Wensheng
AU - Dimaio, J. Michael
AU - Martinez-Lorenzo, Jose A.
N1 - Publisher Copyright:
© 2018 Optical Society of America.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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%.
AB - 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%.
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U2 - 10.1364/BOE.9.001809
DO - 10.1364/BOE.9.001809
M3 - Article
C2 - 29675321
AN - SCOPUS:85044957735
SN - 2156-7085
VL - 9
SP - 1809
EP - 1826
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 4
M1 - #320874
ER -