Deformable Registration

Deformable image registration aims to precisely align images with non-linear distortions, crucial for comparing anatomical structures across different scans or modalities. Current research emphasizes improving accuracy and efficiency through deep learning models, such as U-Nets and Transformers, often incorporating attention mechanisms or multi-scale approaches to capture both local and global features, and exploring novel loss functions and regularization techniques. These advancements are significantly impacting medical image analysis, enabling more accurate diagnoses, improved surgical planning, and facilitating longitudinal studies by allowing for robust comparison of images over time.