MaterialAlphaSAM: An Adaptive Prompting and Domain Adaptation-Based Segmentation Method for the Microstructure of Complex Titanium Alloys

Precise segmentation of high-magnification titanium alloy micrographs under few-shot scenarios remains a non-trivial task, primarily owing to the intricate morphology, heterogeneous discrete distribution, and weak phase boundaries of the primary α phase. To address these issues, this paper presents MaterialAlphaSAM, a lightweight domain-adaptive segmentation framework built upon the Segment Anything Model (SAM). Leveraging SAM’s powerful global context modeling capability, the proposed method incorporates two key modules: a Geometry-Constrained Prompt Prior (GCPP) module and a Domain-Adaptation Adapter (DAA) module. The GCPP module explicitly embeds geometric and morphological priors to generate semantically guided prompts, effectively alleviating prompt redundancy and noise sensitivity. The DAA module performs cross-domain alignment of the encoder features, reducing the domain discrepancy between natural images and metallic microstructures. Extensive experiments demonstrate that both modules consistently boost segmentation performance. On the titanium alloy dataset, MaterialAlphaSAM achieves 89.53% IoU and a 94.40% F1-score, outperforming FCN, UNet, DeepLabV3, PSPNet and the vanilla SAM. It exhibits superior robustness to weak boundaries, fine-scale α phases, and complex background interference.