Skull Stripping

Skull stripping, the automated removal of non-brain tissue from brain MRI scans, is a crucial preprocessing step for neuroimaging analysis. Current research focuses on improving the accuracy and generalizability of skull-stripping algorithms, particularly for diverse populations (e.g., pediatrics) and imaging protocols, employing deep learning architectures like U-Net and its variations (e.g., residual and dense U-Nets) and attention mechanisms to enhance performance. These advancements are vital for improving the efficiency and reliability of neuroimaging studies across various clinical and research settings, enabling more accurate and consistent analyses of brain structure and function.