Deformable Medical Image Registration: From Classical Optimization to Learning-Based Frameworks

PhD Qualifying Examination


Title: "Deformable Medical Image Registration: From Classical Optimization to 
Learning-Based Frameworks"

by

Mr. Yin Lai Lyndon CHAN


Abstract:

Deformable medical image registration is a fundamental problem in medical 
image analysis. Its goal is to establish spatial correspondence between a 
moving image and a fixed image acquired at different time points, from 
different imaging modalities, or across different subjects. Accurate 
deformable registration is essential for many clinical applications, including 
disease monitoring, image-guided intervention, treatment planning, and 
population-based studies. It also supports a wide range of downstream tasks, 
such as image segmentation, atlas construction, tumor tracking, motion 
estimation, and surgical navigation.

Over the past two decades, research in deformable registration has shifted 
from iterative optimization algorithms to learning-based methods. Deep neural 
networks have greatly reduced registration time while achieving accuracy 
comparable to, and in some cases better than, conventional approaches. A 
variety of architectures have been proposed, including convolutional neural 
networks, cascaded networks, transformers, and diffusion-based models. More 
recently, foundation models have attracted growing interest because of their 
potential to improve robustness across datasets and reduce the impact of 
domain shifts.

This survey reviews both the theoretical foundations and recent developments 
in deformable medical image registration. We first introduce the classical 
registration framework, covering transformation models, similarity measures, 
and regularization strategies. We then summarize representative traditional 
and deep learning-based registration methods, discussing their advantages as 
well as their limitations. We introduce the learning-based frameworks in terms 
of learning paradigms, architectures, training objectives, and evaluation 
metrics. Finally, we examine several open challenges, including generalization 
across heterogeneous datasets, pathological registration with missing 
anatomical correspondences, deformation plausibility, and the practical issues 
involved in clinical deployment.


Date:                   Wednesday, 15 July 2026

Time:                   10:00am - 12:00noon

Venue:                  Room 4472
                        Lift 25/26

Committee Members:      Prof. Albert Chung (Supervisor)
                        Dr. Dan Xu (Chairperson)
                        Dr. Xiaomin Ouyang