Deep Learning Based Registration

Deep learning-based image registration aims to automatically align images, a crucial step in many medical imaging applications. Current research focuses on improving accuracy and efficiency through novel network architectures, such as U-Nets, Transformers, and multi-branch networks, often incorporating techniques like probabilistic modeling for uncertainty quantification and spatially-varying regularization for improved deformation field estimation. These advancements address limitations of traditional methods, enabling faster and more robust registration across various modalities and applications, including motion correction, atlas construction, and multi-atlas segmentation. The resulting improvements in image alignment have significant implications for diagnosis, treatment planning, and quantitative image analysis.