Multi Organ Segmentation

Multi-organ segmentation in medical images aims to automatically identify and delineate multiple organs within a single scan, improving diagnostic accuracy and treatment planning. Current research heavily focuses on developing robust deep learning models, employing architectures like U-Nets, Transformers, and hybrid CNN-Transformer approaches, often incorporating attention mechanisms and self-supervised learning to address challenges like data scarcity and class imbalance. These advancements are crucial for improving the efficiency and accuracy of medical image analysis, ultimately leading to better patient care and accelerating medical research.