Hyperspectral imaging for the detection of glioblastoma tumor cells in h&e slides using convolutional neural networks

Samuel Ortega, Martin Halicek, Himar Fabelo, Rafael Camacho, María Plaza De La Luz, Fred Godtliebsen, Gustavo M. Callicó, Baowei Fei

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HIS microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20 ✕ magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.

Original languageEnglish (US)
Article number1911
JournalSensors (Switzerland)
Volume20
Issue number7
DOIs
StatePublished - Apr 1 2020

Keywords

  • Convolutional neural networks
  • Glioblastoma
  • Hyperspectral imaging
  • Medical optics and biotechnology
  • Optical pathology
  • Tissue characterization
  • Tissue diagnostics

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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  • Cite this

    Ortega, S., Halicek, M., Fabelo, H., Camacho, R., Plaza De La Luz, M., Godtliebsen, F., Callicó, G. M., & Fei, B. (2020). Hyperspectral imaging for the detection of glioblastoma tumor cells in h&e slides using convolutional neural networks. Sensors (Switzerland), 20(7), [1911]. https://doi.org/10.3390/s20071911