In this work, we have presented methods for quantifying damage due to cracking and void formation in NMC particles using grey-level analysis of micro-CT images. This study builds upon previous work by Wade et al. by introducing significant improvements in computation speed, handling operando datasets, and enabling automated tracking of grey-level changes across a much larger number of particle instances (approximately 100,000). These advancements were achieved through the development of the GRAPES python toolkit, which includes the updated GREAT2 algorithm for rapid analysis of large tomography datasets. A GUI is also made available.