Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images Techniques and Clinical Applications


Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system. It is distinguished by the appearance of focal lesions in the white and gray matter, which topographically correlate with an individual patient's neurological symptoms and signs. The disease affects people of all ages and can affect both sexes equally. The use of magnetic resonance imaging (MRI) allows for the accurate quantification and categorization of multiple sclerosis lesions, both of which are extremely helpful in disease management. Historically, multiple sclerosis lesions have been manually annotated on 2D MRI slices. This method is not only time consuming, but it also leaves room for inter- and intra-observer error. Recent research has suggested that automated statistical imaging analysis techniques could be used to detect and segment multiple sclerosis lesions based on the intensity of MRI voxels. The heterogeneity of MRI data acquisition techniques, as well as the appearance of MS lesions, makes it difficult for these treatments to be as effective as they could be. Deep Learning techniques have made remarkable strides in the MS lesion segmentation task in recent years. These techniques work by learning complex lesion representations directly from image data. In this article, we provide a comprehensive review of the state-of-the-art automatic statistical and deep-learning methods for MS segmentation, and we discuss current clinical applications as well as potential future ones. In addition, we examine various technical strategies, such as domain adaptation, that can improve the segmentation of multiple sclerosis lesions in actual clinical settings.

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