Automated classification of brain tissue
Comparison between hyperspectral imaging and diffuse reflectance spectroscopy
Marco Lai (Eindhoven University of Technology, Philips Research)
Simon Skyrman (Karolinska University Hospital)
Caifeng Shan (Philips Research)
Elvira Paulussen (Philips Research)
Francesca Manni (Eindhoven University of Technology)
Akash Swamy (Philips Research, TU Delft - Medical Instruments & Bio-Inspired Technology)
Drazenko Babic (Philips Research)
Erik Edstrom (Karolinska University Hospital)
Oscar Persson (Karolinska University Hospital)
Gustav Burstrom (Karolinska University Hospital)
Adrian Elmi-Terander (Karolinska University Hospital)
Benno H.W. Hendriks (TU Delft - Medical Instruments & Bio-Inspired Technology, Philips Research)
Peter H.N. De With (Eindhoven University of Technology)
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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.