White Matter Lesion Segmentation

White matter lesion (WML) segmentation in magnetic resonance imaging (MRI) aims to automatically identify and delineate WMLs, crucial for diagnosing and monitoring neurological diseases like multiple sclerosis. Current research emphasizes improving segmentation accuracy and robustness across different MRI scanners and field strengths, employing deep learning models such as U-Nets and transformers, often incorporating techniques like uncertainty quantification to assess model reliability. These advancements are vital for improving diagnostic accuracy, enabling more precise disease monitoring, and facilitating large-scale studies by addressing challenges like data heterogeneity and limited training data.