Multiple sclerosis is a neurological disease causing a degeneration of myelin around the axons in the central nervous system. This process leaves traces in the form of lesions, which can be distinguished in an MRI examination. It is important to detect these at an early stage to state diagnosis and initiate medication.  In this Master's Thesis, an automatic segmentation algorithm was developed, with the purpose of segmenting possible multiple sclerosis lesions. Secondly, a progression model was developed with the purpose of estimating the state of each individual lesion. The implementation was based on synthetic contrast weighted images, segmentation maps and quantitative relaxation maps produced by SyMRI (SyntheticMR, Linköping, Sweden). The automatic segmentation algorithm has a relatively high sensitivity but low precision, causing a large number of false positives. The algorithm performed better in the cerebrum compared to the cerebellum. The large number of false positives appeared mainly due to partial volume effects, creating hyperintense artifacts in synthetic T2W FLAIR images. A larger amount of data would have been desirable to create a more robust algorithm. The progression model showed promising results, with a clear correlation to the synthetic contrast-weighted images and segmentation maps available in SyMRI. The progression model could be useful in disease monitoring, medical decisions and diagnosis of Multiple Sclerosis.