ArticleJournal of biomedical optics2022
Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral imaging in oral oncology: a scoping review.Frontiers in oral health · 2026Pooled it
- Polarized hyperspectral and polarized light microscopic imaging for enhanced visualization of white blood cells.Journal of biomedical optics · 2026Article
- Development and validation of a high-resolution hyperspectral imaging system for the retina.Journal of biomedical optics · 2026Article
- HMI-LUSC: A Histological Hyperspectral Imaging Dataset for Lung Squamous Cell Carcinoma.Scientific data · 2026Article
- Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- Exploring the role of sample thickness for hyperspectral microscopy tissue discrimination through Monte Carlo simulations.Biomedical optics express · 2025Article
- Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.Journal of cancer research and clinical oncology · 2025Article
- LED-based, real-time, hyperspectral imaging device.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.Scientific reports · 2025Article
- An automatic processing framework for hyperspectral histologic images and benchmark dataset.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Optimization of Transfer Learning of Foundation Models for Hyperspectral Histologic Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Dye amount quantification of Papanicolaou-stained cytological images by multispectral unmixing: spectral analysis of cytoplasmic mucin.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.Biomedicines · 2024Review
- Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.Journal of biomedical optics · 2024Article
- Histological Hyperspectral Glioblastoma Dataset (HistologyHSI-GB).Scientific data · 2024Article
- Polarized hyperspectral microscopic imaging system for enhancing the visualization of collagen fibers and head and neck squamous cell carcinoma.Journal of biomedical optics · 2024Article
- Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2023Article
- Multispectral Imaging Method for Rapid Identification and Analysis of Paraffin-Embedded Pathological Tissues.Journal of digital imaging · 2023Article
- Design and Validation of a Custom-Made Hyperspectral Microscope Imaging System for Biomedical Applications.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
significanceAutomatic, fast, and accurate identification of cancer on histologic slides has many applications in oncologic pathology.
aimThe purpose of this study is to investigate hyperspectral imaging (HSI) for automatic detection of head and neck cancer nuclei in histologic slides, as well as cancer region identification based on nuclei detection. APPROACH: A customized hyperspectral microscopic imaging system was developed and used to scan histologic slides from 20 patients with squamous cell carcinoma (SCC). Hyperspectral images and red, green, and blue (RGB) images of the histologic slides with the same field of view were obtained and registered. A principal component analysis-based nuclei segmentation method was developed to extract nuclei patches from the hyperspectral images and the coregistered RGB images. Spectra-based support vector machine and patch-based convolutional neural networks (CNNs) were implemented for nuclei classification. The CNNs were trained with RGB patches (RGB-CNN) and hyperspectral patches (HSI-CNN) of the segmented nuclei and the utility of the extra spectral information provided by HSI was evaluated. Furthermore, cancer region identification was implemented by image-wise classification based on the percentage of cancerous nuclei detected in each image.
resultsRGB-CNN, which mainly used the spatial information of nuclei, resulted in a 0.81 validation accuracy and 0.74 testing accuracy. HSI-CNN, which utilized the spatial and spectral features of the nuclei, showed significant improvement in classification performance and achieved 0.89 validation accuracy as well as 0.82 testing accuracy. Furthermore, the image-wise cancer region identification based on nuclei detection could generally improve the cancer detection rate.
conclusionsWe demonstrated that the morphological and spectral information contribute to SCC nuclei differentiation and that the spectral information within hyperspectral images could improve classification performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.