Image Registration
Image registration aims to precisely align images from different sources or time points, a crucial preprocessing step for many medical and remote sensing applications. Current research emphasizes developing faster, more accurate, and robust registration methods, focusing on deep learning architectures like transformers and diffusion models, as well as incorporating probabilistic uncertainty quantification and techniques to handle multimodal data and complex deformations. These advancements are improving the accuracy and efficiency of various downstream tasks, including medical image analysis, surgical planning, and geospatial data integration.
Papers
An Adaptive Correspondence Scoring Framework for Unsupervised Image Registration of Medical Images
Xiaoran Zhang, John C. Stendahl, Lawrence Staib, Albert J. Sinusas, Alex Wong, James S. Duncan
Heteroscedastic Uncertainty Estimation for Probabilistic Unsupervised Registration of Noisy Medical Images
Xiaoran Zhang, Daniel H. Pak, Shawn S. Ahn, Xiaoxiao Li, Chenyu You, Lawrence Staib, Albert J. Sinusas, Alex Wong, James S. Duncan