  Minerals, metals, and plastics are indispensable for a functioning modern
society. Yet, their supply is limited causing a need for optimizing ore
extraction and recuperation from recyclable materials.Typically, those
processes must be meticulously adapted to the precise properties of the
processed materials. Advancing our understanding of these materials is thus
vital and can be achieved by crushing them into particles of micrometer size
followed by their characterization. Current imaging approaches perform this
analysis based on segmentation and characterization of particles imaged with
computed tomography (CT), and rely on rudimentary postprocessing techniques to
separate touching particles. However, their inability to reliably perform this
separation as well as the need to retrain methods for each new image, these
approaches leave untapped potential to be leveraged. Here, we propose
ParticleSeg3D, an instance segmentation method able to extract individual
particles from large CT images of particle samples containing different
materials. Our approach is based on the powerful nnU-Net framework, introduces
a particle size normalization, uses a border-core representation to enable
instance segmentation, and is trained with a large dataset containing particles
of numerous different sizes, shapes, and compositions of various materials. 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 particle samples, enabling
substantially higher scalability of experiments than existing methods. Our code
and dataset are made publicly available.
