The implantation of intracoronary stents is currently the standard approach for the treatment of coronary atherosclerotic disease. The widespread adoption of this technology has boosted an intensive research activity in this domain, with continuous improvements in the design of these devices, aiming at reducing problems of restenosis (re-narrowing of the stented segment) and thrombosis (sudden occlusion due to thrombus formation). Recently, a new, light-based intracoronary imaging modality, optical coherence tomography (OCT), was developed and introduced into clinical practice. Due to its very high axial resolution (10–15 μm), it allows for in vivo evaluation of both stent strut apposition and neointima coverage (a marker of healing of the treated segment). As such, it provides valuable information on proper stent deployment, on the behaviour of different stent types in-vivo and on the effect of new types of stents (e.g. drug-eluting stents) on vessel wall healing. However, the major drawback of the current OCT methodology is that analysis of these images requires a tremendous amount of—currently manual—post-processing. In this manuscript, an algorithm is presented that allows for fully automated analysis of stent strut apposition and coverage in coronary arteries. The vessel lumen and stent struts are automatically detected and segmented through analysis of the intensity profiles of the A-lines. From these data, apposition and coverage can then be measured automatically. The algorithm was validated using manual assessments by two experienced operators as a reference. High Pearson’s correlation coefficients were found (R = 0.96–0.97) between the automated and manual measurements while Bland–Altman analysis showed no significant bias with good limits of agreement. As such, it was shown that the presented algorithm provides a robust and fast tool to automatically estimate apposition and coverage of stent struts in in-vivo OCT pullbacks. This will be important for the integration of this technology in clinical routine and for the analysis of datasets of larger clinical trials.