Hyperspectral superpixel-wise glioblastoma tumor detection in histological samples

Samuel Ortega, Himar Fabelo, Martin Halicek, Rafael Camacho, María de la Luz Plaza, Gustavo M. Callicó, Baowei Fei

Research output: Contribution to journalArticlepeer-review

Abstract

The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. Alinear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level.

Original languageEnglish (US)
Article number4448
JournalApplied Sciences (Switzerland)
Volume10
Issue number13
DOIs
StatePublished - Jul 1 2020

Keywords

  • Digital pathology
  • Glioblastoma (GB)
  • Hyperspectral imaging
  • Machine learning
  • Optics diagnosis
  • Superpixel
  • Tissue characterization
  • Tissue diagnostics

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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