Medical Segmentation

Medical image segmentation aims to automatically delineate anatomical structures or lesions within medical scans, improving diagnostic accuracy and efficiency. Current research emphasizes improving segmentation accuracy and robustness, particularly using advanced architectures like U-Net and its variants, transformers, and capsule networks, often incorporating techniques such as multi-modal fusion, active learning, and uncertainty quantification. These advancements are crucial for improving clinical workflows, enabling more precise diagnoses, and facilitating personalized treatment planning.