Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical coherence tomography (OCT) has been recently introduced for intravascular imaging and assessment of coronary artery disease. Two-dimensional (2-D) cross-sectional OCT images of coronary arteries play a crucial role to characterize the internal structure of the tissues. Adjacent images could be compounded; however, they might not fully match due to motion, which is a major hurdle for analyzing longitudinally each tissue in 3-D. The aim of this study is to develop a robust tissue-matching-based motion correction approach from a sequence of 2-D intracoronary OCT images. Our motion correction technique is based on the correlation between deep features obtained from a convolutional neural network (CNN) for each frame of a sequence. The optimal transformation of each frame is obtained by maximizing the similarity between the tissues of reference and moving frames. The results show a good alignment of the tissues after applying CNN features and determining the transformation parameters.