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.

翻译：高光谱成像（HSI）为医学成像提供了丰富的光谱信息，但由于数据限制和硬件差异而面临重大挑战。我们提出了SAMSA，一种新颖的交互式分割框架，它将RGB基础模型与光谱分析相结合。SAMSA高效利用用户点击来指导RGB分割和光谱相似性计算。该方法通过一种独特的光谱特征融合策略解决了HSI分割中的关键限制，该策略独立于光谱波段数量和分辨率运行。在公开数据集上的性能评估显示，在神经外科数据集上实现了81.0%的单点击和93.4%的五点击DICE分数，在术中猪高光谱数据集上实现了81.1%的单点击和89.2%的五点击DICE分数。实验结果证明了SAMSA在少样本和零样本学习场景中的有效性，并且仅需极少的训练示例。我们的方法能够无缝集成具有不同光谱特征的数据集，为高光谱医学图像分析提供了一个灵活的框架。