Automated classification of brain tissue: comparison between hyperspectral imaging and diffuse reflectance spectroscopy

Marco Lai, Simon Skyrman, Caifeng Shan, Elvira Paulussen, Francesca Manni, Akash Swamy, Drazenko Babic, Erik Edstrom, Oscar Persson, Gustav Burstrom, Adrian Elmi-Terander, Benno H.W. Hendriks, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
Number of pages7
ISBN (Electronic)9781510633971
DOIs
Publication statusPublished - 16 Mar 2020
Event2020 SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, United States
Duration: 16 Feb 202019 Feb 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11315
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2020 SPIE Medical Imaging
CountryUnited States
CityHouston
Period16/02/2019/02/20

Keywords

  • Brain surgery
  • Diffuse reflectance spectroscopy
  • Hyperspectral imaging
  • Image classification
  • Image-guided surgery
  • Machine learning
  • Neurosurgery
  • Tissue classification

Fingerprint Dive into the research topics of 'Automated classification of brain tissue: comparison between hyperspectral imaging and diffuse reflectance spectroscopy'. Together they form a unique fingerprint.

  • Cite this

    Lai, M., Skyrman, S., Shan, C., Paulussen, E., Manni, F., Swamy, A., Babic, D., Edstrom, E., Persson, O., Burstrom, G., Elmi-Terander, A., Hendriks, B. H. W., & de With, P. H. N. (2020). Automated classification of brain tissue: comparison between hyperspectral imaging and diffuse reflectance spectroscopy. In B. Fei, & C. A. Linte (Eds.), Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling [113151X] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11315). SPIE. https://doi.org/10.1117/12.2548754