Skip to main content

HISTOLOGYHSI-GB

HistologyHSI-GB | Hyperspectral Histological Images for Diagnosis of Human Glioblastoma

DOI: 10.7937/z1k6-vd17 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Brain Human 13 Histopathology Glioblastoma 582GB Public, Complete 2024/05/24

Summary

Hyperspectral imaging technology combines the main features of two existing technologies: conventional imaging and spectroscopy. Thus, hyperspectral cameras make it possible to analyze, at the same time and in a non-contact way, the morphological features and chemical composition of the objects captured. The information provided by hyperspectral imaging can be used to detect patterns, cells, or biomarkers to identify diseases. There are different alternatives for processing them and there is a lack of publicly available datasets of medical hyperspectral images. To the best of our knowledge, this is the first open access dataset containing histological hyperspectral images of glioblastoma brain tumors, which can be set as a benchmark for researchers to compare their approaches.

This dataset is composed of a single histological slide from each of 13 subjects., and several hyperspectral images of areas highlighted as relevant by the pathologists (this number varies for each slide) captured from each slide. . The database is composed of 469 annotated hyperspectral images from 13 histological slides, having a spatial dimension of 800 × 1004 pixels and a spectral dimension of 826 spectral channels. The format of the hyperspectral images is ENVI, the standard format for the storage of hyperspectral images. The ENVI format consists of a flat-binary raster file with an accompanying ASCII header file. The data are stored in band-interleaved-by-line format. In addition, dark and white references were captured to perform a calibration of the raw image, which is a standard procedure in hyperspectral image processing.

The slides were stained with hematoxylin and eosin and captured using a custom hyperspectral microscopic system at 20× magnification. The ground-truth annotation for this dataset is the diagnosis of the slides (tumor or not tumor) performed by skilled histopathologists after the visual examination of the stained slides, according to the World Health Organization classification of tumors of the nervous system. As far as we are concerned, there are no commercial hyperspectral whole slide scanners. Also, the availability of hyperspectral microscopes is still limited in the market.  

The microscope is an Olympus BX-53 (Olympus, Tokyo, Japan). The hyperspectral camera is a Hyperspec® VNIR A-Series from HeadWall Photonics (Fitchburg, MA, USA), which is based on an imaging spectrometer coupled to a charge-coupled device sensor, the Adimec-1000m (Adimec, Eindhoven, Netherlands). This hyperspectral system works in the visual and near-infrared spectral range from 400 to 1000 nm with a spectral resolution of 2.8 nm, sampling 826 spectral channels, and 1004 spatial pixels. The push-broom camera performs a spatial scanning to acquire a hyperspectral cube with a mechanical stage (SCAN, Märzhäuser, Wetzlar, Germany) attached to the microscope, which provides an accurate movement of the slides. The objective lenses are from the LMPLFLN family (Olympus, Tokyo, Japan), optimized for infrared observations.

Data Access

Version 1: Updated 2024/05/24

Title Data Type Format Access Points Subjects Studies Series Images License
Tissue Slide Images Histopathology PNG and ENVI
Download requires IBM-Aspera-Connect plugin
13 482 CC BY 4.0

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Ortega, S., Fabelo, H., Quintana-Quintana, L., Leon, R., Plaza, M.d.l.L., Camacho, R., & Callico, G. M. (2024). Hyperspectral Histological Images for Diagnosis of Human Glioblastoma (HistologyHSI-GB) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/Z1K6-VD17

Acknowledgements

  • This work was supported by the Spanish Government and European Union as part of the TALENT-HExPERIA (HypErsPEctRal Imaging for Artificial intelligence applications) project (PID2020-116417RB-C42). Moreover, this work was completed while Laura Quintana and Raquel Leon were beneficiary of the pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Consejería de Economía, Conocimiento y Empleo”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%)) and, Himar Fabelo was beneficiary of the FJC2020-043474-I funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU/PRTR”.

Additional Publications Related to this Work

The Collection authors suggest the below will give context to this dataset:

  • Ortega, S.; Fabelo, H.; Halicek, M.; Camacho, R.; Plaza, M.d.l.L.; Callicó, G.M.; Fei, B. Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples. Appl. Sci. 2020, 10, 4448. https://doi.org/10.3390/app10134448
  • Ortega, S.; Halicek, M.; Fabelo, H.; Camacho, R.; Plaza, M.d.l.L.; Godtliebsen, F.; M. Callicó, G.; Fei, B. Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks. Sensors 2020, 20, 1911. https://doi.org/10.3390/s20071911

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.