Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy
Freija Geldof (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Behdad Dashtbozorg (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Benno H.W. Hendriks (TU Delft - Medical Instruments & Bio-Inspired Technology, Philips Research)
Henricus J.C.M. Sterenborg (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, Amsterdam UMC)
Theo J.M. Ruers (University of Twente, Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
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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.
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