ArticleScientific reports2021
VNIR-NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.Journal of biomedical optics · 2025Review
- Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques.Frontiers in oncology · 2025Review
- The LMIT: Light-mediated minimally-invasive theranostics in oncology.Theranostics · 2024Review
- Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection.NPJ precision oncology · 2023Article
- Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2023Article
- Delineation and detection of breast cancer using novel label-free fluorescence.BMC medical imaging · 2023Article
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Impact of Pre- and Post-Processing Steps for Supervised Classification of Colorectal Cancer in Hyperspectral Images.Cancers · 2023Article
- Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study.Frontiers in neuroscience · 2023Article
- Evaluation of Hyperspectral Imaging for Follow-Up Assessment after Revascularization in Peripheral Artery Disease.Journal of clinical medicine · 2022Article
- Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery.Frontiers in bioengineering and biotechnology · 2022Review
- Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review.Frontiers in surgery · 2022Review
- Registration of hyperspectral images and mass spectrometry data for the correlation of tissue optical spectra and molecular profiles.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400-1000 nm] and near-infrared (NIR) [900-1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435-1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR-NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.