Medical Image Segmentation Task

Medical image segmentation aims to automatically delineate specific anatomical structures or pathologies within medical images, aiding diagnosis and treatment planning. Current research heavily focuses on improving accuracy and efficiency using various architectures, including U-Net variations, Transformers, and hybrid models that combine convolutional neural networks with attention mechanisms or state-space models, often incorporating techniques like test-time training and prompt engineering. These advancements are crucial for improving the speed and accuracy of medical image analysis, ultimately leading to better patient care and accelerating medical research.