Journal Article

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

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

2020

Overview

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 HSI 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.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Brain - diagnostic imaging / Brain - pathology / Brain cancer / Cameras / Classification / Deep Learning / Feasibility studies / Glioblastoma - diagnosis / Glioblastoma - pathology / Humans / Hyperspectral Imaging / Image Processing, Computer-Assisted / Methods / Nerve Net / Neural networks / Neural Networks, Computer / Software / Support vector machines