Longitudinal analyses in Multiple Sclerosis are often performed to assess disease progression and evaluate treatment response. The number of new and enlarged lesions as well as total lesion volume variations over time are imaging biomarkers used in MS follow-up assessment. Here, we evaluate the performance of an in-house prototype algorithm for lesion detection and volume estimation in a longitudinal scenario. Our algorithm can be run with or without partial volume modelling. Both detection and volume estimation improved using the partial volume model with respect to manual delineations, especially in small lesions and at lesion borders.