Multi Atlas Segmentation

Multi-atlas segmentation (MAS) is a medical image analysis technique that leverages multiple annotated images (atlases) to segment a target image without extensive manual labeling. Current research focuses on integrating deep learning architectures, such as U-Net variations and transformers, with MAS to improve accuracy and efficiency, often incorporating multimodal data and addressing challenges like limited annotated data through techniques like self-supervised pretraining and label smoothing. This approach is significant for improving the speed and accuracy of medical image segmentation across various applications, including brain analysis (fetal and adult), enabling more efficient and precise diagnoses and quantitative assessments of brain development and disease.