Image registration is a fundamental technique in medical imaging that involves aligning two or more images into a common coordinate system, enabling meaningful comparisons and analysis across time, subjects, or modalities. This talk provides an overview of registration and its role in the medical domain, where registration supports applications such as longitudinal patient monitoring, image-guided surgery, and multi-modal image fusion. It also highlights recent advances in learning-based approaches, with a particular focus on neural fields. Finally, the application of registration to anatomical atlases is examined, discussing how it facilitates population-level analysis and the construction of reference models.
Vasiliki Sideri-Lampretsa is a Ph.D. student at the Institute for Artificial Intelligence and Informatics in Medicine at the Technical University of Munich (TUM). She received her Diploma in Electrical and Computer Engineering at Aristotle University of Thessaloniki and her Master’s degree in Computer Science at TUM. She was a research assistant at the Interdisciplinary Research Lab (IFL) at TUM, where she also conducted her Master’s thesis. During her Master’s thesis, she focused on employing deep learning to tackle the inverse problem of predicting the speed of sound using ultrasound raw channel data aquired from simulations. Currently, her research interest lies in the field of image registration, neural fields and geometric deep learning.
