Quasiconformal model with CNN features for large deformation image registration

TL;DR

This paper introduces a novel large deformation image registration method combining quasiconformal theory with CNN features, eliminating the need for manual landmarks and ensuring bijectivity, thus improving registration accuracy and robustness.

Contribution

It proposes a new quasiconformal energy model that integrates CNN features for landmark-free, bijective image registration, bridging mathematical theory and machine learning.

Findings

Effective registration without landmarks

Guaranteed bijectivity via quasiconformal theory

Demonstrated improved performance in experiments

Abstract

Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling and are prone to error. In recent years, there has been a surge of interest in the use of machine learning for image registration. In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN). More specifically, we propose a quasiconformal energy model with a novel fidelity term that incorporates the features extracted using a pre-trained CNN, thereby allowing us to obtain meaningful registration results without any guidance of prescribed landmarks. Moreover, unlike many prior image registration methods, the bijectivity of our method is guaranteed by quasiconformal theory. Experimental results are presented to demonstrate the effectiveness of the proposed method. More broadly, our work sheds light on how rigorous mathematical theories and practical machine learning approaches can be integrated for developing computational methods with improved performance.