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Multiple-Sclerosis (MS) is a disease that shows patterns of inflammation (lesions) in the white matter of the brain, which are detectable with MRI. Automatic, accurate segmentation of lesions is very important for studying the progress of the disease, monitoring treatments, diagnosing patients and assessing clinical trials for new treatments. Current approaches, however, fail to be sufficiently accurate and robust, leaving most segmentations to be done manually with associated problems of speed and repeatability. Machine learning techniques show promising initial results but optimising their performance in conjunction with the image acquisition has not been explored. This project aims to apply existing and novel machine learning techniques to the problem of white-matter lesion segmentation as well as exploring how the performance of these methods is affected by the characteristics of the input images and features derived from them. Optimisation of the image acquisition methods, within clinically-relevant constraints, will also be investigated.