Medical Image Segmentation

Medical image segmentation aims to automatically delineate specific anatomical structures or regions of interest within medical images, facilitating accurate diagnosis and treatment planning. Current research heavily focuses on improving segmentation accuracy and efficiency using advanced architectures like U-Net and its variants, Vision Transformers, and Large Language Models, often incorporating techniques such as multi-scale feature extraction, attention mechanisms, and test-time training. These advancements are crucial for improving diagnostic capabilities, accelerating clinical workflows, and enabling more precise and personalized medicine. Furthermore, research is actively addressing challenges like limited annotated data through semi-supervised learning and the use of foundation models for improved generalization across different imaging modalities and clinical settings.