Paving the way for future mineral processing and recycling technologies through large-scale analysis of particulate samples
Understanding and quantifying the exact composition of mineral samples paves the way towards advanced methodologies that not only increase the effectiveness of ore processing but also enable future recycling technologies. To this end, samples consisting of ground particles embedded into an epoxy matrix are imaged with computed tomography (CT) at micrometer resolution. Due to the sheer amount of particles in each sample, automated image analysis is quintessential to unlocking the potential of his technique. As part of this collaboration we develop a precise and robust instance segmentation method that enables large scale analysis of samples.
Publications
4725570
Particulate sampling
1
https://helmholtz-imaging.de/apa-bold-title.csl
50
date
desc
573
https://helmholtz-imaging.de/wp-content/plugins/zotpress/
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Gupta, S., da Assuncao Godinho, J. R., Gotkowski, K., & Isensee, F. (2024). Standardized and semiautomated workflow for 3D characterization of liberated particles. Powder Technology, 433, 119159. https://doi.org/10.1016/j.powtec.2023.119159
Gotkowski, K., Gupta, S., Godinho, J. R. A., Tochtrop, C. G. S., Maier-Hein, K. H., & Isensee, F. (2024). ParticleSeg3D: A scalable out-of-the-box deep learning segmentation solution for individual particle characterization from micro CT images in mineral processing and recycling. Powder Technology, 434, 119286. https://doi.org/10.1016/j.powtec.2023.119286