用SAM改进3D岩石孔隙图像分割，减少重训练需求。
SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images
- 将SAM编码器与Mamba体积建模结合，实现跨尺度特征交互。
- 在不同岩性、流体和扫描条件下性能持平或超越现有方法。
- 适合需快速分析多条件3D多相孔隙数据的研究者。
可靠分割多相孔隙尺度岩心X射线图像对于量化流体饱和度、连通性和界面几何至关重要。然而，当前3D分割方法通常依赖特定数据集，一旦岩性、流体模式、扫描仪或采集条件变化，就需要重新训练或大量微调。像分割一切模型（SAM）这样的基础模型虽具备强大的2D边界先验，但无法直接应用于3D数据。本文提出SAMamba3D，一种参数高效框架，通过将几乎冻结的SAM编码器与基于Mamba的体积上下文建模及渐进式跨尺度特征交互相结合，实现可泛化的3D孔隙尺度分割。在砂岩和碳酸盐岩数据集上，面对不同流体、润湿性及扫描条件，SAMamba3D性能达到或优于现有3D基线方法，显著减少针对特定案例的再训练需求。生成的分割图像保留了物理意义明确的描述符，包括流体饱和度、连通性与界面形态，从而支持对大规模3D多相图像更可靠、快速的分析。
原文摘要 · Abstract (English)
Reliable segmentation of multiphase pore-scale X-ray images of rocks is necessary to quantify fluid saturation, connectivity, and interfacial geometry. However, current 3D segmentation methods are typically dataset-specific, requiring retraining or extensive fine-tuning whenever rock type, fluid pattern, scanner, or acquisition conditions change. Foundation models such as the Segment Anything Model (SAM) provide strong 2D boundary priors, but they are not directly applicable to 3D data. We present SAMamba3D, a parameter-efficient framework that adapts a largely frozen SAM encoder to generalizable 3D pore-scale segmentation by coupling it with Mamba-based volumetric context modeling and progressive cross-scale feature interaction. For sandstone and carbonate datasets, with different fluids, wettability, and scanning conditions, SAMamba3D matches or outperforms current 3D baselines while reducing the need for case-specific retraining. The resulting segmented images preserve physically meaningful descriptors, including fluid saturation, connectivity, and interface morphology, enabling more reliable and rapid analysis of large 3D multiphase images.
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