Title
Automated classification of brain tissue: Comparison between hyperspectral imaging and diffuse reflectance spectroscopy
Author
Lai, Marco (Philips Research; Eindhoven University of Technology)
Skyrman, Simon (Karolinska University Hospital)
Shan, Caifeng (Philips Research)
Paulussen, Elvira (Philips Research)
Manni, Francesca (Eindhoven University of Technology)
Swamy, A. (TU Delft Medical Instruments & Bio-Inspired Technology; Philips Research) 
Babic, Drazenko (Philips Research)
Edstrom, Erik (Karolinska University Hospital)
Persson, Oscar (Karolinska University Hospital)
Burstrom, Gustav (Karolinska University Hospital)
Elmi-Terander, Adrian (Karolinska University Hospital)
Hendriks, B.H.W. (TU Delft Medical Instruments & Bio-Inspired Technology; Philips Research)
De With, Peter H.N. (Eindhoven University of Technology)
Contributor
Fei, Baowei (editor)
Linte, Cristian A. (editor)
Date
2020
Abstract
In neurosurgery, technical solutions for visualizing the border between healthy brain and tumor tissue is of great value, since they enable the surgeon to achieve gross total resection while minimizing the risk of damage to eloquent areas. By using real-time non-ionizing imaging techniques, such as hyperspectral imaging (HSI), the spectral signature of the tissue is analyzed allowing tissue classification, thereby improving tumor boundary discrimination during surgery. More particularly, since infrared penetrates deeper in the tissue than visible light, the use of an imaging sensor sensitive to the near-infrared wavelength range would also allow the visualization of structures slightly beneath the tissue surface. This enables the visualization of tumors and vessel boundaries prior to surgery, thereby preventing the damaging of tissue structures. In this study, we investigate the use of Diffuse Reflectance Spectroscopy (DRS) and HSI for brain tissue classification, by extracting spectral features from the near infra-red range. The applied method for classification is the linear Support Vector Machine (SVM). The study is conducted on ex-vivo porcine brain tissue, which is analyzed and classified as either white or gray matter. The DRS combined with the proposed classification reaches a sensitivity and specificity of 96%, while HSI reaches a sensitivity of 95% and specificity of 93%. This feasibility study shows the potential of DRS and HSI for automated tissue classification, and serves as a fjrst step towards clinical use for tumor detection deeper inside the tissue.
Subject
Brain surgery
Diffuse reflectance spectroscopy
Hyperspectral imaging
Image classification
Image-guided surgery
Machine learning
Neurosurgery
Tissue classification
To reference this document use:
http://resolver.tudelft.nl/uuid:d8999959-2b92-4c6e-9644-21c3c50da8fd
DOI
https://doi.org/10.1117/12.2548754
Publisher
SPIE, Bellingsham, WA, USA
ISBN
9781510633971
Source
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 11315
Event
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 2020-02-16 → 2020-02-19, Houston, United States
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2020 Marco Lai, Simon Skyrman, Caifeng Shan, Elvira Paulussen, Francesca Manni, A. Swamy, Drazenko Babic, Erik Edstrom, Oscar Persson, Gustav Burstrom, Adrian Elmi-Terander, B.H.W. Hendriks, Peter H.N. De With