White Matter Tract Segmentation

White matter tract segmentation aims to automatically identify and delineate individual fiber bundles in the brain using diffusion MRI, crucial for understanding brain connectivity and neurological disorders. Current research focuses on improving the accuracy and generalizability of deep learning models, employing techniques like multi-task learning, scaled residual bootstrapping, and geometric deep learning frameworks to handle variations in data quality and anatomical differences across individuals. These advancements are significantly impacting neuroimaging research by enabling more efficient and accurate analysis of brain structure, facilitating studies of brain development, disease, and the relationship between brain structure and cognitive function.