Multiple Sclerosis Lesion Segmentation

Multiple sclerosis (MS) lesion segmentation aims to automatically identify and delineate MS lesions in brain MRI scans, improving diagnostic efficiency and disease monitoring. Current research heavily utilizes deep learning models, particularly U-Net architectures and their variants, often incorporating strategies like self-ensembling, continual learning, and federated learning to address challenges posed by data heterogeneity and limited labeled data. These advancements aim to create more robust and generalizable segmentation models, ultimately leading to more accurate and reliable assessment of MS disease progression and treatment response.