Inverse Consistency by Construction for Multistep Deep Registration
Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networks in a way that preserves inverse consistency. This multi-step approach also allows for inverse-consistent coarse to fine registration. We evaluate our technique on synthetic 2-D data and four 3-D medical image registration tasks and obtain excellent registration accuracy while assuring inverse consistency.
Code (2)
Tasks
Image RegistrationMedical Image RegistrationSimilar Papers 제목 키워드 기반
SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images. Popular class…
Deep LearningDeformable Medical Image RegistrationImage RegistrationMedical Image RegistrationCoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems
Diffusion models have been demonstrated as strong priors for solving general inverse problems. Most existing Diffusion model-based Inverse Problem Solvers (DIS) employ a plug-and-play approach to guide the sampling traje…
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness regularizers for image registration fail to integrate population statistics…
Image RegistrationMedical Image AnalysisMORPHUnsupervised Image Registration$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based m…
Computed Tomography (CT)Image RegistrationMedical Image RegistrationOptical Flow EstimationGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-bas…
Image RegistrationMedical Image Registration