Deep Registration

Deep registration uses deep learning to efficiently and accurately align medical images, aiming to improve the precision and speed of various medical image analysis tasks. Current research focuses on developing robust and generalizable models, including transformer-based networks and diffusion models, that can handle diverse modalities, anatomical regions, and large deformations, often incorporating techniques like coarse-to-fine registration and inverse consistency constraints. These advancements are significantly impacting medical image analysis by enabling more accurate quantitative analysis, improved segmentation, and more efficient workflows for applications such as radiotherapy planning and disease monitoring.