Subcortical Segmentation

Subcortical segmentation aims to automatically identify and delineate specific subcortical brain structures from magnetic resonance imaging (MRI) scans. Current research focuses on improving the accuracy and efficiency of segmentation methods, employing advanced deep learning architectures such as 3D convolutional neural networks (CNNs), CNN-Transformer hybrids, and diffusion models, often incorporating multi-modal MRI data to enhance contrast and detail. These advancements are crucial for improving the diagnosis and monitoring of neurological disorders, enabling more precise neurosurgical planning, and facilitating quantitative analyses of brain structure in large-scale studies.