Efficient and accurate extraction of microstructures in micrographs of materials is essential in process optimization and the exploration of structure-property relationships. Deep learning-based image segmentation techniques that rely on manual annotation are laborious and time-consuming and hardly meet the demand for model transferability and generalization on various source images. 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. In this paper, we propose MatSAM, a general and efficient microstructure extraction solution based on SAM. A simple yet effective point-based prompt generation strategy is designed, grounded on the distribution and shape of microstructures. Specifically, in an unsupervised and training-free way, it adaptively generates prompt points for different microscopy images, fuses the centroid points of the coarsely extracted region of interest (ROI) and native grid points, and integrates corresponding post-processing operations for quantitative characterization of microstructures of materials. For common microstructures including grain boundary and multiple phases, MatSAM achieves superior zero-shot segmentation performance to conventional rule-based methods and is even preferable to supervised learning methods evaluated on 16 microscopy datasets whose micrographs are imaged by the optical microscope (OM) and scanning electron microscope (SEM). Especially, on 4 public datasets, MatSAM shows unexpected competitive segmentation performance against their specialist models. We believe that, without the need for human labeling, MatSAM can significantly reduce the cost of quantitative characterization and statistical analysis of extensive microstructures of materials, and thus accelerate the design of new materials.

翻译：材料显微图像中微结构的快速精确提取对工艺优化及构效关系研究至关重要。基于深度学习的图像分割技术依赖人工标注，不仅耗时费力，且难以满足模型在不同来源图像上的可迁移性与泛化性需求。作为具有强大深度特征表征与零样本泛化能力的大型视觉模型，Segment Anything Model（SAM）为图像分割提供了全新解决方案。本文提出MatSAM——一种基于SAM的通用高效微结构提取方案。我们设计了一种简单有效的点提示生成策略，该策略基于微结构的分布与几何形态特征。具体而言，该方法以无监督且免训练的方式，自适应地为不同显微图像生成提示点，融合粗提取感兴趣区域（ROI）的质心点与原生网格点，并集成相应后处理操作，实现材料微结构的定量表征。针对晶界、多相组织等常见微结构，MatSAM在16个光学显微镜（OM）与扫描电子显微镜（SEM）显微图像数据集上的零样本分割性能优于传统规则方法，甚至超过监督学习方法。特别地，在4个公开数据集上，MatSAM的分割性能与专有模型相比展现出意外的竞争力。我们相信，无需人工标注的MatSAM可显著降低大规模材料微结构定量表征与统计分析的成本，从而加速新材料设计进程。