Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre. 

Citation

BibTex format

@inproceedings{Roddan:2026:10.1007/978-3-032-05114-1_46,
author = {Roddan, A and Czempiel, T and Xu, C and Elson, DS and Giannarou, S},
doi = {10.1007/978-3-032-05114-1_46},
pages = {478--488},
publisher = {Springer},
title = {SAMSA: Segment anything model enhanced with spectral angles for hyperspectral interactive medical image segmentation},
url = {http://dx.doi.org/10.1007/978-3-032-05114-1_46},
year = {2026}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Hyperspectral imaging (HSI) provides rich spectral information for medical imaging, yet encounters significant challenges due to data limitations and hardware variations. We introduce SAMSA, a novel interactive segmentation framework that combines an RGB foundation model with spectral analysis. SAMSA efficiently utilizes user clicks to guide both RGB segmentation and spectral similarity computations. The method addresses key limitations in HSI segmentation through a unique spectral feature fusion strategy that operates independently of spectral band count and resolution. Performance evaluation on publicly available datasets has shown 81.0% 1-click and 93.4% 5-click DICE on a neurosurgical and 81.1% 1-click and 89.2% 5-click DICE on an intraoperative porcine hyperspectral dataset. Experimental results demonstrate SAMSA’s effectiveness in few-shot and zero-shot learning scenarios and using minimal training examples. Our approach enables seamless integration of datasets with different spectral characteristics, providing a flexible framework for hyperspectral medical image analysis.
AU  - Roddan,A
AU  - Czempiel,T
AU  - Xu,C
AU  - Elson,DS
AU  - Giannarou,S
DO  - 10.1007/978-3-032-05114-1_46
EP  - 488
PB  - Springer
PY  - 2026///
SN  - 0302-9743
SP  - 478
TI  - SAMSA: Segment anything model enhanced with spectral angles for hyperspectral interactive medical image segmentation
UR  - http://dx.doi.org/10.1007/978-3-032-05114-1_46
UR  - https://doi.org/10.1007/978-3-032-05114-1_46
ER  -
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