EXTENDED ABSTRACT: With the rapid development of artificial intelligence, materials informatics, and materials genome engineering, scientists use quantitative microstructure analysis to establish the intrinsic relationships between composition, structure, processing, and performance. Therefore, utilizing image processing technologies for quantitative analysis of material is important. The technical of materials microscopic image research is illustrated in Figure 1. Acquiring microscopic images of the material through two-dimensional sections and employing image processing techniques enables the reconstruction of three-dimensional structural models, which are used for qualitative and quantitative characterization. Deep learning-based artificial intelligence technology has a stronger ability to extract features. It can identify more complex details, which are often overlooked by the human eye. At the same time, it can handle large volumes of image data, increasing the speed of image processing and analysis. This report reviews the application of artificial intelligence in materials microscopic image analysis such as ultra-large image stitching, multi-focus image fusion, three-dimensional reconstruction, and quantitative characterization. Moreover, we have created a platform for analyzing microscopic images which integrates various annotation tools and deep-learning image recognition models. This platform supports researchers to rapidly and independently create artificial intelligence models, accelerating the practical application of AI technology. 

Keywords：Materials Microscopic Image Analysis; Artificial Intelligence; ntelligent Image Analysis Platform