White Matter Hyperintensity Segmentation

White matter hyperintensity (WMH) segmentation aims to automatically identify and delineate areas of abnormally high intensity in brain MRI scans, indicative of neurological conditions like stroke and multiple sclerosis. Current research focuses on improving the accuracy and robustness of segmentation using deep learning models, including U-Nets, transformers, and novel architectures incorporating spatial attention mechanisms and persistent homology for improved lesion counting. These advancements are crucial for quantitative analysis of WMH, enabling more precise diagnosis, disease monitoring, and ultimately, better patient outcomes. The development of robust and generalizable methods is particularly important given the variability in MRI acquisition parameters across different scanners and field strengths.