Osteoarthritis (OA) is a degenerative joint disease affecting a significant portion of the population. No approved therapies for OA exist, and advanced methods to evaluate preclinical outcomes are urgently needed. OA is a whole joint disease, and micro-computed tomography is frequently used to analyze bone structures in preclinical models. Still, there is a limited number of methods for cartilage analysis. Accurate detection of cartilage surface defects is critical when assessing OA progression and therapeutic outcomes. Current methods like histological grading are time-intensive and limited by their two-dimensional nature. This chapter introduces the Cartilage Roughness Score (CRS) from a contrast-enhanced micro-computed tomography (CEμCT) as a detailed, three-dimensional analysis of cartilage surfaces. This chapter will provide an outline of the sample preparation and actual analysis. Possible sample preparation needs to maintain surface integrity. Most of the processing steps are automated, including cartilage segmentation and iterative polynomial surface fitting to approximate healthy cartilage. Localized angular deviations are calculated within defined windows, providing a spatially detailed map of surface irregularities. Outputs such as CRS(mean), CRS > X°, and sum CRS are discussed as measures for assessing surface degradation and tracking lesion progression. The advantages of CRS include its ability to analyze the entire joint surface, reducing the bias introduced by histological sampling. The method is adaptable to various imaging modalities as long as resolution criteria are met. This chapter details the practical steps for implementing CRS, highlights its potential in OA research, and discusses areas for further refinement and automation.