Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

• Type of Material: Article
• Authors: Samuel Ortega, University of Texas Dallas; Martin Halicek, University of Texas Dallas; Himar Fabelo, University of Las Palmas de Gran Canaria; Rafael Camacho, University of Las Palmas de Gran Canaria; Maria de la Luz Plaza, University of Las Palmas de Gran Canaria; Fred Godtliebsen, University of Norway; Gustavo M. Callico, University of Las Palmas de Gran Canaria; Baowei Fei, Emory University
• Language: English
• Date: 2020-04-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/s20071911
• Title of Journal or Parent Work: Sensors
• Volume: 20
• Issue: 7
• Grant/Funding Information: This research is supported in part by the Cancer Prevention and Research Institute of Texas (CPRIT) grant RP190588.; This work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%)).; This work has been supported by the Spanish Government through PLATINO project (TEC2017-86722-C4-4-R), and the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHaCA project “Hyperspectral identification of Brain tumors” under Grant Agreement ProID2017010164.
• Keywords: convolutional neural networks; Technology; optical pathology; Nuclei; Chemistry, Analytical; Engineering; Science & Technology; Chemistry; tissue characterization; Physical Sciences; glioblastoma; medical optics and biotechnology; hyperspectral imaging; tissue diagnostics; Engineering, Electrical & Electronic; Instruments & Instrumentation
• Research Categories: Biology, Microbiology; Chemistry, Biochemistry; Health Sciences, Pathology; Engineering, Biomedical

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.