Hyperspectral (HS) imaging (HSI) technology combines the main features of two existing technologies: imaging and spectroscopy. This allows to analyse simultaneously the morphological and chemical attributes of the objects captured by a HS camera. In recent years, the use of HSI provides valuable insights into the interaction between light and biological tissues, and makes it possible to detect patterns, cells, or biomarkers, thus, being able to identify diseases. This work presents the HistologyHSI-GB dataset, which contains 469 HS images from 13 patients diagnosed with brain tumours, specifically glioblastoma. The slides were stained with haematoxylin and eosin (H&E) and captured using a microscope at 20× power magnification. Skilled histopathologists diagnosed the slides and provided image-level annotations. The dataset was acquired using custom HSI instrumentation, consisting of a microscope equipped with an HS camera covering the spectral range from 400 to 1000 nm.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196658 | PMC |
| http://dx.doi.org/10.1038/s41597-024-03510-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution.
Sensors (Basel)
November 2024
CeMOS Research and Transfer Center, University of Applied Science, 68163 Mannheim, Germany.
This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies.
View Article and Find Full Text PDFCervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.
Chem Biomed Imaging
September 2024
Department of Electrical and Computer Engineering, University of Houston, Houston, Texas 77030, United States.
Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms.
View Article and Find Full Text PDFHyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study.
Cancer Med
September 2024
Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Article Synopsis
- The study explores the use of hyperspectral imaging (HSI) to diagnose colorectal polyps and cancers during colonoscopy, aiming to improve the accuracy of polyp classification.
- A deep learning model was developed using 144,900 images to distinguish between different types of polyps, showing high sensitivity and specificity in identifying neoplastic conditions.
- The findings suggest that HSI can enhance diagnostic capabilities, particularly for less experienced endoscopists, potentially reducing healthcare costs through a more effective resect-and-discard approach.
Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.
Healthc Technol Lett
August 2024
Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging.
View Article and Find Full Text PDFDeep learning for rapid virtual H&E staining of label-free glioma tissue from hyperspectral images.
Comput Biol Med
September 2024
National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China; Wenzhou Institute, University of Chinese Academy of Sciences, Jinlian Road 1, Wenzhou, 325001, China. Electronic address:
Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and specialist pathologists, rendering it expensive, labor-intensive, and time-consuming. In view of these considerations, we combine the deep learning method and hyperspectral imaging technique, aiming at accurately and rapidly converting the hyperspectral images into virtual H&E staining images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!