3D Medical Image

3D medical image analysis focuses on extracting meaningful information from volumetric datasets like CT and MRI scans to improve diagnosis, treatment planning, and research. Current research emphasizes developing efficient and accurate segmentation methods, often leveraging advanced architectures such as transformers and diffusion models, alongside techniques like self-supervised learning and prompt-based approaches (e.g., adaptations of the Segment Anything Model). These advancements aim to reduce the need for extensive manual annotation, improve the speed and accuracy of analysis, and ultimately enhance patient care and accelerate medical discoveries. The field is also actively exploring methods for handling uncertainty quantification and addressing challenges related to data scarcity and computational cost.