Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
This addresses the need for clinically meaningful and anatomically plausible image registration in medical applications, representing a novel method for a known bottleneck.
The paper tackled the problem of ensuring topology-preserving transformations in deep learning-based deformable image registration for medical images, achieving superior performance over current techniques through a novel conformal-invariant hyperelastic regulariser and coordinate MLPs.
Deformable image registration is a fundamental task in medical image analysis and plays a crucial role in a wide range of clinical applications. Recently, deep learning-based approaches have been widely studied for deformable medical image registration and achieved promising results. However, existing deep learning image registration techniques do not theoretically guarantee topology-preserving transformations. This is a key property to preserve anatomical structures and achieve plausible transformations that can be used in real clinical settings. We propose a novel framework for deformable image registration. Firstly, we introduce a novel regulariser based on conformal-invariant properties in a nonlinear elasticity setting. Our regulariser enforces the deformation field to be smooth, invertible and orientation-preserving. More importantly, we strictly guarantee topology preservation yielding to a clinical meaningful registration. Secondly, we boost the performance of our regulariser through coordinate MLPs, where one can view the to-be-registered images as continuously differentiable entities. We demonstrate, through numerical and visual experiments, that our framework is able to outperform current techniques for image registration.