SAMSA: Segment anything model enhanced with spectral angles for hyperspectral interactive medical image segmentation
File(s)
Accepted version
Author(s)
Roddan, Alfie
Czempiel, Tobias
Xu, Chi
Elson, Daniel S
Giannarou, Stamatia
Type
Conference Paper
Abstract
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.
Date Issued
2026-01-01
Date Acceptance
2025-09-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2026, 15968, pp.478-488
ISBN
9783032051134
ISSN
0302-9743
Publisher
Springer
Start Page
478
End Page
488
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
15968
Copyright Statement
© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
MICCAI 2025
Publication Status
Published
Start Date
2025-09-23
Finish Date
2025-09-27
Coverage Spatial
Daejeon, South Korea
Date Publish Online
2025-09-21