1 research outputs found
[Work in progress] Scalable, out-of-the box segmentation of individual particles from mineral samples acquired with micro CT
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