Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characteristics. Intermediate phases-key microstructural constituents-are pivotal in regulating mechanical and functional properties. However, intermediate phase segmentation in zinc alloy microstructures faces formidable challenges: scarce annotated datasets, low contrast, difficulty detecting small targets, and heterogeneous morphologies. To this end, we construct IPSM-Bench, the largest high-quality dataset for zinc-alloy intermediate phase segmentation. Furthermore, we propose SCoP-SAM, a new Spatial Context Prior-guided SAM method that leverages the gradient structure and grayscale properties of intermediate phases to capture spatial context priors and incorporates them into the entire SAM encoding-decoding process, improving segmentation performance. Based on the proposed IPSM-Bench, we establish a new benchmark for intermediate phase segmentation to systematically evaluate state-of-the-art (SOTA) methods and advance research on zinc alloy microstructure analysis. Extensive experiments on IPSM-Bench and additional public alloy benchmarks demonstrate that our SCoP-SAM not only achieves SOTA performance for zinc-alloy intermediate phase segmentation but also generalizes remarkably well to other alloy scenarios.

翻译：锌基合金是不可或缺的新兴可吸收金属生物材料，其宏观性能由微观结构特征决定。中间相作为关键的微观结构组成，在调控力学与功能特性中起核心作用。然而，锌合金微观结构中的中间相分割面临严峻挑战：标注数据集稀缺、对比度低、小目标检测困难以及形态异质性。为此，我们构建了IPSM-Bench——目前最大的锌合金中间相分割高质量数据集。此外，提出SCoP-SAM方法，这是一种新的空间上下文先验引导的SAM方法，通过利用中间相的梯度结构与灰度属性捕捉空间上下文先验，并将其嵌入整个SAM编解码过程，从而提升分割性能。基于所提出的IPSM-Bench，我们建立了中间相分割的基准测试体系，系统评估现有最优（SOTA）方法，推动锌合金微观结构分析研究。在IPSM-Bench及额外公开合金基准上的大量实验表明，我们的SCoP-SAM不仅实现了锌合金中间相分割的SOTA性能，而且对其他合金场景具有显著的泛化能力。