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, 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 imaged with a micro CT, 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 methods for each new image, these approaches leave untapped potential to be leveraged. Here, we propose ParticleSeg3D, 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 ParticleSeg3D 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组成部分。然而其供应有限，因此需要优化从矿石和可回收材料中的勘探与提取过程。通常，这些工艺必须根据所处理颗粒的精确特性进行细致调整，包括对其形状、外观以及整体材料成分的全面表征。现行方法基于显微CT成像颗粒的体素分割与表征进行分析，并依赖基础的后处理技术分离相互接触的颗粒。然而，由于这些方法无法可靠地实现分离，且针对每张新图像都需要重新训练或配置方法，其潜在效能未能得到充分发挥。本文提出ParticleSeg3D实例分割方法，能够从嵌入环氧树脂基质的矿物样本大尺寸显微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，利用边界-核心表征实现实例分割，并通过包含多种不同材料和矿物颗粒的大规模数据集进行训练。我们证明ParticleSeg3D可开箱即用地应用于多种颗粒类型，包括训练集中未覆盖的材料和外观样本。因此，将该方法应用于新矿物样本时无需额外的人工标注和重新训练，相比现有方法实现了更高的实验可扩展性。我们的代码和数据集已公开提供。