Segmentation is the assigning of a semantic class to every pixel in an image and is a prerequisite for various statistical analysis tasks in materials science, like phase quantification, physics simulations or morphological characterization. The wide range of length scales, imaging techniques and materials studied in materials science means any segmentation algorithm must generalise to unseen data and support abstract, user-defined semantic classes. Trainable segmentation is a popular interactive segmentation paradigm where a classifier is trained to map from image features to user drawn labels. SAMBA is a trainable segmentation tool that uses Meta's Segment Anything Model (SAM) for fast, high-quality label suggestions and a random forest classifier for robust, generalizable segmentations. It is accessible in the browser (https://www.sambasegment.com/) without the need to download any external dependencies. The segmentation backend is run in the cloud, so does not require the user to have powerful hardware.

翻译：分割是为图像中每个像素分配语义类别，是材料科学中多项统计分析任务（如相量化、物理模拟或形态学表征）的前置步骤。材料科学中研究的尺度范围、成像技术和材料类型极为广泛，这意味着任何分割算法都必须能泛化至未见数据，并支持抽象的、用户定义的语义类别。可训练分割是一种流行的交互式分割范式，其通过训练分类器将图像特征映射至用户绘制的标注。SAMBA是一种可训练分割工具，它利用Meta公司的Segment Anything Model (SAM)实现快速高质量标注建议，并采用随机森林分类器实现稳健且可泛化的分割。用户可通过浏览器直接访问该工具（https://www.sambasegment.com/），无需下载任何外部依赖项。分割后端部署于云端，因此用户无需配备高性能硬件。