Accurate and efficient extraction of microstructures in microscopic images of materials plays a critical role in the exploration of structure-property relationships and the optimization of process parameters. Deep learning-based image segmentation techniques that rely on manual annotation are time-consuming and labor-intensive and hardly meet the demand for model transferability and generalization. Segment Anything Model (SAM), a large visual model with powerful deep feature representation and zero-shot generalization capabilities, has provided new solutions for image segmentation. However, directly applying SAM to segmenting microstructures in microscopic images of materials without human annotation cannot achieve the expected results, as the difficulty of adapting its native prompt engineering to the dense and dispersed characteristics of key microstructures in materials microscopy images. In this paper, we propose MatSAM, a general and efficient microstructure extraction solution based on SAM. A new point-based prompts generation strategy is designed, grounded on the distribution and shape of materials microstructures. It generates prompts for different microscopic images, fuses the prompts of the region of interest (ROI) key points and grid key points, and integrates post-processing methods for quantitative characterization of materials microstructures. For common microstructures including grain boundary and phase, MatSAM achieves superior segmentation performance to conventional methods and is even preferable to supervised learning methods evaluated on 18 materials microstructures imaged by the optical microscope (OM) and scanning electron microscope (SEM). We believe that MatSAM can significantly reduce the cost of quantitative characterization of materials microstructures and accelerate the design of new materials.

翻译：材料显微图像中微观结构的准确高效提取，对于探索结构-性能关系及优化工艺参数至关重要。依赖人工标注的深度学习图像分割技术耗时耗力，且难以满足模型迁移性与泛化性的需求。具备强大深度特征表征与零样本泛化能力的大视觉模型Segment Anything Model（SAM）为图像分割提供了新方案。然而，直接将未经过人工标注的SAM应用于材料显微图像中的微观结构分割，无法取得预期效果，原因在于其原生提示工程难以适配材料显微图像中关键微观结构密集离散的分布特征。本文提出MatSAM——一种基于SAM的通用高效微观结构提取方案。我们设计了一种基于材料微观结构分布与形态的新型点提示生成策略，能够针对不同显微图像生成提示，融合感兴趣区域（ROI）关键点与网格关键点的提示，并集成用于材料微观结构定量表征的后处理方法。针对晶界与相界等常见微观结构，MatSAM在18种光学显微镜（OM）与扫描电子显微镜（SEM）成像的材料微观结构评估中，取得了优于传统方法的分割性能，甚至优于监督学习方法。我们相信MatSAM能够显著降低材料微观结构定量表征的成本，加速新材料的设计进程。