Multiple sclerosis is the most common neurological disorder in young adults, with more than 2 million affected people worldwide. The disease causes formation of lesions in the central nervous system where the myelin sheaths of neurons are damaged. These lesions can be visualized with different MR sequences, depending on the individual development of each lesion. The detection and segmentation of those lesions offers an important diagnostic value in the medical practice and makes up a time consuming step in the development of new treatments, as large drug trials usually require the manual delineation of lesions in thousands of brain scans.

The so called lesion load is a measure often taken into account when conducting MS studies, where individual lesions are counted and the total amount is noted per patient. Another popular measure is the lesion volume, but due to the cumbersome process of acquiring it manually by segmenting it voxel-wise, it is not always gathered. Both of these measures are highly rater dependent, since two raters would either completely ignore or accept a lesion, as well as most likely disagree about the lesion boundaries of individual lesions if no clear guideline has been defined beforehand, resulting in a significant inter-rater variability.

Given an automatic measure, studies could be compared on the same grounds. We hence investigate different automatic methods to accomplish this task. Our most successful developments for brain tissue segmentation as well as lesion segmentation are both located in the area of deep learning, achieving state of the art segmentations in both domains.

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