High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging.
Overview
- Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), 35012 Las Palmas de Gran Canaria, Spain
- Research Unit, Hospital Universitario de Gran Canaria Dr. Negrín, 35010 Las Palmas de Gran Canaria, Spain
- Institute for Applied Microelectronics (IUMA), Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain(C.V.); (R.L.);(G.M.C.)
- Instituto de Investigación Sanitaria de Canarias (IISC), 35012 Las Palmas de Gran Canaria, Spain
- Department of Electrical Engineering, Eindhoven University of Technology (TU/e), 5612 Eindhoven, The Netherlands(F.M.); (S.Z.)
- Department of Neurosurgery, Hospital Universitario de Gran Canaria Dr. Negrín, 35010 Las Palmas de Gran Canaria, Spain(J.M.M.); (J.F.P.)
- Department of Neurosurgery, Hospital Universitario 12 Octubre, 28041 Madrid, Spain(A.L.); (L.J.-R.)
- Department of Surgery, Medicine Faculty, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Instituto de Investigaciones Sanitarias (imas12), 28041 Madrid, Spain
Abstract
Hyperspectral (HS) imaging has proven to be a promising intraoperative tool for tissue discrimination. However, obtaining representative datasets for intraoperative imaging remains challenging due to the complexity of surgical workflows and the sensitivity of the operating environments. Hence, developing new methods for cross-system feature adaptation could address this limitation. This work proposes a method for mapping high-resolution spectral data into lower-resolution sensor-conditioned domains, generating synthetic HS data that replicate the spectral features of the target system. We assessed the mapped data using public HS datasets and quantified spectral similarities using different metrics. Additionally, we evaluated the method with a HS classification framework for an intraoperative brain tumour classification problem. Results demonstrate that the synthetic data achieve high spectral alignment to original and actual data, captured with the target system. The brain tumour classification results show comparable performance between data modalities. Overall, this work provides a way to adapt existing HS datasets to complement newly acquired data, accelerating the development of artificial intelligence algorithms. This is particularly relevant in medical research, and especially in neurosurgery, where the complexity of acquisition environments limits the collection of large datasets.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
No file of the authors' code could be read here: it is described below, and read at its source.
git.iuma.ulpgc.es/stratum/public
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The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data Availability Statement
The data presented in this study are available in the IUMA-ULPGC HSI Database and HSI Human Brain Database at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 3 funders, 34 references.
Cite
This paper
Santana-Nunez, J., Verbers, M., Vega, C., Manni, F., Leon, R., Morera Molina, J., F Piñeiro, J., Lagares, A., Jimenez-Roldan, L., Callico, G. M., Zinger, S., & Fabelo, H. (2026). High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging. Bioengineering (Basel, Switzerland), 13(5), 549. https://
BibTeX
@article{santananunez202
author = {Santana-Nunez, Javier and Verbers, Max and Vega, Carlos and Manni, Francesca and Leon, Raquel and Morera Molina, Jesús and F Piñeiro, Juan and Lagares, Alfonso and Jimenez-Roldan, Luis and Callico, Gustavo M and Zinger, Svitlana and Fabelo, Himar},
title = {{High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging}},
journal = {Bioengineering (Basel, Switzerland)},
year = {2026},
month = may,
volume = {13},
number = {5},
pages = {549},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2306-5354},
doi = {10.3390/
url = {https://
pmid = {42194306},
pmcid = {PMC13203705}
}
RIS
TY - JOUR
AU - Santana-Nunez, Javier
AU - Verbers, Max
AU - Vega, Carlos
AU - Manni, Francesca
AU - Leon, Raquel
AU - Morera Molina, Jesús
AU - F Piñeiro, Juan
AU - Lagares, Alfonso
AU - Jimenez-Roldan, Luis
AU - Callico, Gustavo M
AU - Zinger, Svitlana
AU - Fabelo, Himar
TI - High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging
T2 - Bioengineering (Basel, Switzerland)
J2 - Bioengineering (Basel)
PY - 2026
DA - 2026/
VL - 13
IS - 5
SP - 549
SN - 2306-5354
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
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