Standardized and semiautomated workflow for 3D characterization of liberated particles

MSPaCMAn is the recently developed workflow that does the mineralogical quantification of individual particles using its histograms while considering the effects of partial volume artefacts in interphases at particle level detail. This paper demonstrates and validates the new developments in the MSPaCMAn workflow, aiming to minimize user bias and enhance the accuracy of MSPaCMAn.

Here, in the new developments of MSPaCMAn workflow, firstly, the recently developed deep learning method, namely ParticleSeg3D, is employed to distinguish particles from the background. Secondly, the particle's size and shape information are considered along with its histogram to classify and quantify the mineral phases in liberated particles. After the new developments, the detection limit of MSPaCMAn to characterize small and thin liberated particles is enhanced. Experimental results demonstrate the effectiveness of the MSPaCMAn's updated workflow. It was found that the mineralogical composition calculated by the MSPaCMAn's updated workflow was precise, with the highest coefficient of variance of 6.56%. Moreover, the mineralogical composition determined by MSPaCMAn's updated workflow had low variance across three different reconstruction parameters and two different voxel sizes (5.5 μm and 10 μm). Comparisons with other quantification methods highlight the accuracy of MSPaCMAn's updated workflow to determine the mineralogical compositions. When analyzing a test sample consisting of quartz, calcite, fluorite, and lepidolite, MSPaCMAn's updated workflow achieved the highest mineralogical deviation of only 7.73% from the reference mineralogy. In contrast, the random forest algorithm resulted in a deviation of 51.47%, while the manual thresholding method yielded a deviation of 41.46%.
Overall, these findings emphasize the reliability and accuracy of MSPaCMAn's updated workflow in quantifying mineralogical composition.