Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy

Freija Geldof, Behdad Dashtbozorg, Benno H.W. Hendriks, Henricus J.C.M. Sterenborg, Theo J.M. Ruers

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
13 Downloads (Pure)

Abstract

During oncological surgery, it can be challenging to identify the tumor and establish adequate resection margins. This study proposes a new two-layer approach in which diffuse reflectance spectroscopy (DRS) is used to predict the top layer thickness and classify the layers in two-layered phantom and animal tissue. Using wavelet-based and peak-based DRS spectral features, the proposed method could predict the top layer thickness with an accuracy of up to 0.35 mm. In addition, the tissue types of the first and second layers were classified with an accuracy of 0.95 and 0.99. Distinguishing multiple tissue layers during spectral analyses results in a better understanding of more complex tissue structures encountered in surgical practice.

Original languageEnglish
Article number1698
Number of pages12
JournalScientific Reports
Volume12
Issue number1
DOIs
Publication statusPublished - 2022

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