用光谱角度增强分割模型，仅靠点击就能精准分割高光谱医学图像。
SAMSA: Segment Anything Model Enhanced with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation
- 结合RGB基础模型与光谱分析，用用户点击同时指导图像和光谱分割。
- 1次点击达81.0% DICE，5次点击达93.4% DICE，跨数据集表现稳定。
- 不依赖光谱波段数量，适合小样本或无训练的高光谱医学图像分析。
高光谱成像（HSI）为医学影像提供丰富的光谱信息，但受限于数据量少和硬件差异。本文提出SAMSA，一种融合RGB基础模型与光谱分析的交互式分割框架。该方法通过用户点击同时引导RGB分割与光谱相似性计算。独特的光谱特征融合策略独立于光谱波段数量与分辨率，有效解决HSI分割中的关键挑战。在公开数据集上的评估显示：神经外科数据集上1次点击达81.0%、5次点击达93.4% DICE；猪术中高光谱数据集上1次点击达81.1%、5次点击达89.2% DICE。实验表明，SAMSA在少样本和零样本场景下均表现优异，仅需极少训练样本。该方法可无缝整合不同光谱特性的数据集，为高光谱医学图像分析提供灵活框架。
原文摘要 · Abstract (English)
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.
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