3D Medical Image Segmentation

3D medical image segmentation aims to automatically identify and delineate anatomical structures within three-dimensional medical scans, facilitating accurate diagnosis and treatment planning. Current research emphasizes developing efficient and accurate segmentation models, focusing on architectures like U-Nets, Transformers, and state-space models (e.g., Mamba), often incorporating techniques like self-attention and efficient feature fusion to improve performance and reduce computational costs. These advancements are crucial for improving the speed and accuracy of medical image analysis, ultimately leading to better patient care and accelerating medical research.