Magnetic Resonance Imaging Segmentation

Magnetic resonance imaging (MRI) segmentation aims to automatically delineate specific anatomical structures or lesions within MRI scans, facilitating accurate diagnosis and treatment planning. Current research heavily utilizes deep learning, employing architectures like nnU-Net and transformers, often incorporating self-supervised learning techniques to address the scarcity of labeled data and improve model generalization across different MRI modalities and centers. These advancements are crucial for improving the efficiency and accuracy of medical image analysis, leading to better patient care and accelerating research in various medical fields.