Background: Magnetic resonance imaging (MRI) is used to follow-up multiple sclerosis (MS) and evaluate disease progression and therapy response via lesion quantification. However, there is a lack of automated post-processing techniques to quantify individual MS lesion change. Objective: The present study developed a secondary post-processing algorithm for MS lesion segmentation routine to quantify individual changes in volume over time. Methods: An Automatic Follow-up of Individual Lesions (AFIL) algorithm was developed to process time series of pre-segmented binary lesion masks. The resulting consistently labelled lesion masks allowed for the evaluation of individual lesion volumes. Algorithm performance testing was executed in seven early MS patients with four MRI visits, and MS experienced readers verified the accuracy. Results: AFIL distinguished 328 individual MS lesions with a 0.9% error rate to track persistent or new lesions based on expert assessment. A total of 121 new lesions evolved within the observed time period. The proportional courses of 69.1% lesions in the persistent lesion population exhibited varying volume, 16.9% exhibited stable volume, 3.4% exhibiting continuously increasing, and 0.5% exhibited continuously decreasing volume. Conclusion: This algorithm tracked individual lesions to automatically create an individual lesion growth profile of MS patients. This approach may allow for characterization of patients based on their individual lesion progression.