Advancements in Medical Semantic Segmentation: A Review of U-Net and Its Variants

The U-Net model has emerged as a leading architecture in the domain of medical semantic segmentation, demonstrating remarkable efficacy. Its influence has spurred the development of variant networks, notably Unetr and Swin Unetr, which have further advanced the state-of-the-art. These variants have achieved groundbreaking results in a variety of clinical medical image segmentation tasks and challenges, pushing the boundaries of automated medical image analysis.