用光谱角度提示改进SAM，提升医学高光谱图像分割精度
SAMSA 2.0: Prompting Segment Anything with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation
- 结合光谱角度与空间信息，早期融合多维特征
- 无需重训练，分割精度比RGB模型高3.8%，比旧方法高3.1%
- 在少样本和噪声环境下表现优异，适合临床实际场景
我们提出SAMSA 2.0，一种用于高光谱医学影像的交互式分割框架，通过引入光谱角度提示，利用光谱相似性与空间线索协同引导分割任何模型（SAM）。这种早期融合光谱信息的方法显著提升了在多样光谱数据集上的分割准确率与鲁棒性。无需重新训练，SAMSA 2.0相比仅使用RGB的模型最多提升3.8%的Dice分数，相较于先前的光谱融合方法最多提升3.1%。该方法增强了少样本与零样本下的性能，在临床成像中常见的低数据量与噪声环境下展现出强泛化能力。
原文摘要 · Abstract (English)
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.
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