Brain Segmentation

Brain segmentation, the automated partitioning of brain images into distinct anatomical regions, aims to improve efficiency and accuracy in neuroimaging analysis. Current research emphasizes developing robust deep learning models, such as 3D convolutional neural networks (CNNs) and transformer-based architectures, often incorporating techniques like patch-based processing and multi-resolution approaches to handle high-resolution images and diverse anatomical structures. These advancements are crucial for improving the speed and accuracy of clinical diagnoses, particularly in conditions like Alzheimer's disease and brain tumors, and for facilitating large-scale neuroimaging studies. Furthermore, significant effort is dedicated to addressing challenges like data scarcity, domain adaptation across different scanners and datasets, and enhancing model interpretability.