Brain Extraction

Brain extraction, the process of isolating brain tissue from surrounding structures in medical images like MRI scans, is crucial for accurate neuroimaging analysis. Current research heavily utilizes deep learning, particularly U-Net and its variations, along with generative models and other advanced architectures like Attention U-Nets and CycleGANs, to automate this process and improve accuracy, especially in challenging cases such as infant brains or those with pathologies. These advancements aim to reduce the time and expertise required for preprocessing, enabling faster and more efficient analysis for both research and clinical applications, including improved diagnosis and treatment planning. The development of robust, generalizable methods that handle diverse image qualities and pathologies remains a key focus.