Herein, we present the segmentation of the pristine and XFC-cycled graphite anode reconstructed volumes. We discuss the conversion of dual-tomography grayscale imaging data to colorized segmented images using the 2D bivariate histogram phase segmentation. We used graylevel values of the reconstructed and registered neutron and X-ray µCT images to construct the 2D histogram of the pristine (Figure ) and XFC-cycled anodes (Figure ). Note that the graylevel neutron and X-ray values represent the experimentally determined neutron (𝜇 !_6E4 ) and X-ray (𝜇 %_6E4 ) linear attenuation coefficients respectively, having arbitrary units (a.u.), although the experimental values are roughly calibrated to the calculated attenuation lengths in cm -1 . During segmentation, we used 𝜇 !_#$# and 𝜇 %_#$# from Table to guide the material identification in the reconstructed volumes. First, we observe the relative neutron and X-ray contrast between our sample materials (e.g., graphite and copper) to locate their peaks in the 2D histogram. Then, we compare the 𝜇 !_#$# and 𝜇 %_#$# of a material of interest to its relative experimental graylevel values from approximately the center of the 2D peak for voxel assignment. Any significant discrepancies between the relative database-derived and graylevel values could be due to various factors including: mixed material compositions and/or porosity within each voxel, unknown systematic experimental error, inherent NeXT calibration error, choice of CT reconstruction algorithms, and effect of metal artifacts such as X-ray beam hardening from Cu. As mentioned in the previous section, our results show that it is extremely challenging to segment the anode materials using individual neutron and X-ray data alone. Instead, combining the neutron and X-ray data into a 2D bivariate histogram and using database-derived attenuation coefficients as a guide have enabled easier and more informed segmentation of the battery materials within the imaged volumes.