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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
TitelMedical Imaging 2020
SubtitelImage-Guided Procedures, Robotic Interventions, and Modeling
RedacteurenBaowei Fei, Cristian A. Linte
UitgeverijSPIE
Aantal pagina's7
ISBN van elektronische versie9781510633971
DOI's
StatusGepubliceerd - 16 mrt 2020
Evenement2020 SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, Verenigde Staten van Amerika
Duur: 16 feb 202019 feb 2020

Publicatie series

NaamProceedings of SPIE - The International Society for Optical Engineering
Volume11315
ISSN van geprinte versie0277-786X
ISSN van elektronische versie1996-756X

Congres

Congres2020 SPIE Medical Imaging
LandVerenigde Staten van Amerika
StadHouston
Periode16/02/2019/02/20

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  • Citeer dit

    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 (editors), 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