Brain Tissue Segmentation

Brain tissue segmentation, the automated identification and delineation of different brain tissues in MRI scans, aims to improve the efficiency and accuracy of neuroimaging analysis. Current research focuses on addressing challenges like age-related variations in tissue appearance, scanner heterogeneity, and limited annotated data, employing techniques such as normalizing flows for image harmonization, generative models for data augmentation, and deep learning architectures including U-Nets, transformers, and diffusion models. These advancements are crucial for accelerating research in neurodevelopmental disorders, cognitive impairment, and other brain conditions by enabling large-scale, robust analysis of brain structure and its changes over time. Improved segmentation methods facilitate more accurate diagnosis, treatment monitoring, and a deeper understanding of brain development and disease.