We present SAMSA 2.0, an interactive segmentation framework for hyperspectral medical imaging that introduces spectral angle prompting to guide the Segment Anything Model (SAM) using spectral similarity alongside spatial cues. This early fusion of spectral information enables more accurate and robust segmentation across diverse spectral datasets. Without retraining, SAMSA 2.0 achieves up to +3.8% higher Dice scores compared to RGB-only models and up to +3.1% over prior spectral fusion methods. Our approach enhances few-shot and zero-shot performance, demonstrating strong generalization in challenging low-data and noisy scenarios common in clinical imaging.

翻译：我们提出了SAMSA 2.0，这是一个用于高光谱医学成像的交互式分割框架，它引入了光谱角提示，以利用光谱相似性和空间线索共同指导Segment Anything Model（SAM）。这种光谱信息的早期融合能够在多样的光谱数据集上实现更准确、更鲁棒的分割。无需重新训练，与仅使用RGB的模型相比，SAMSA 2.0的Dice分数最高可提升+3.8%，与先前的光谱融合方法相比最高可提升+3.1%。我们的方法增强了少样本和零样本性能，在临床成像中常见的、具有挑战性的低数据量和噪声场景中展现了强大的泛化能力。