Whole Brain Segmentation

Whole brain segmentation, the automated partitioning of brain MRI scans into distinct anatomical regions, aims to accelerate and improve the analysis of brain structure and function. Current research focuses on developing robust deep learning models, including U-Net variations and transformer-based architectures like UNesT, that can handle the variability inherent in clinical MRI data, often incorporating strategies like region-based processing and multi-resolution capabilities. These advancements enable more accurate and efficient analysis of large-scale datasets, facilitating quantitative morphometry for studies of brain development, aging, and disease, and ultimately improving clinical diagnosis and treatment planning.