Minerals are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing their exploration and extraction both from ores and recyclable materials. Typically, these processes must be meticulously adapted to the precise properties of the processed particles, requiring an extensive characterization of their shapes, appearances as well as the overall material composition. Current approaches perform this analysis based on bulk segmentation and characterization of particles, and rely on rudimentary postprocessing techniques to separate touching particles. However, due to their inability to reliably perform this separation as well as the need to retrain or reconfigure most methods for each new image, these approaches leave untapped potential to be leveraged. Here, we propose an instance segmentation method that is able to extract individual particles from large micro CT images taken from mineral samples embedded in an epoxy matrix. Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, makes use of a border-core representation to enable instance segmentation and is trained with a large dataset containing particles of numerous different materials and minerals. We demonstrate that our approach can be applied out-of-the box to a large variety of particle types, including materials and appearances that have not been part of the training set. Thus, no further manual annotations and retraining are required when applying the method to new mineral samples, enabling substantially higher scalability of experiments than existing methods. Our code and dataset are made publicly available.

翻译：矿物是现代社会中不可或缺的材料。然而其供应有限，需优化矿石及可回收材料中矿物的勘探与提取工艺。通常，这些流程必须根据处理颗粒的精确特性进行细致调整，要求对颗粒形状、外观及整体材料组成进行广泛表征。现有方法基于颗粒的整体分割与表征进行分析，并依赖基础后处理技术分离接触颗粒。但由于这些方法无法可靠实现颗粒分离，且大多数方法需针对每幅新图像重新训练或配置，导致其潜在应用价值未能充分释放。本文提出一种实例分割方法，能够从嵌入环氧树脂基体的矿物样本所获取的大尺寸微CT图像中提取单个颗粒。该方法基于强大的nnU-Net框架，引入颗粒尺寸归一化技术，采用边界-核心表征实现实例分割，并使用包含多种不同材料与矿物类型颗粒的大型数据集进行训练。实验表明，该方法可即用型地应用于包括训练集中未出现材料与外观在内的各类颗粒。因此，将该方法应用于新矿物样本时无需额外人工标注与重新训练，相比现有方法实现了显著更高的实验可扩展性。我们的代码与数据集已公开提供。