ArticleBiomedical optics express2019
Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Clinical applications of hyperspectral imaging in gastroenterology and hepatology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Pooled it
- 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
- Possibility of determining high quantitative fecal occult blood on stool surface using hyperspectral imaging.Journal of gastroenterology · 2025Article
- A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases.Sensors (Basel, Switzerland) · 2024Article
- High-speed scanless entire bandwidth mid-infrared chemical imaging.Nature communications · 2023Article
- Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Bibliometric analysis of the current status and trends on medical hyperspectral imaging.Frontiers in medicine · 2023Review
- Visible near-infrared hyperspectral imaging and supervised classification for the detection of small intestinal necrosis tissue in vivo.Biomedical optics express · 2022Article
- Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging.Journal of biomedical optics · 2022Article
- Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging.Journal of biomedical optics · 2022Article
- Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging.Scientific reports · 2022Article
- New Intraoperative Imaging Tools and Image-Guided Surgery in Gastric Cancer Surgery.Diagnostics (Basel, Switzerland) · 2022Review
- Hyperspectral Microscopic Imaging for the Detection of Head and Neck Squamous Cell Carcinoma on Histologic Slides.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Hyperspectral microscopy combined with DAPI staining for the identification of hepatic carcinoma cells.Biomedical optics express · 2021Article
- Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
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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.