To address potential errors during image processing, all image processing steps were automated using Python scripts to ensure repeatability. Visual validation of particle labels and segmentation results confirmed their consistency. For both case studies, datasets were cleaned by removing edge particles and inconsistent labels, ensuring that only reliable data were included in the analysis. Furthermore, care was taken during data comparison to normalize grey-levels and reduce the impact of sample-to-sample variability. These measures collectively enhanced the robustness of the analysis and minimised the impact of experimental errors on the results. We believe these to be best practices when applying this method to similar data.