$\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration

TL;DR

NePhi introduces a neural deformation model for medical image registration that is memory-efficient, faster, and maintains high accuracy and deformation regularity, outperforming voxel-based methods especially in multi-resolution settings.

Contribution

NePhi presents a functional representation of deformations enabling flexible, efficient, and accurate medical image registration with approximately diffeomorphic transformations.

Findings

Achieves comparable accuracy to voxel-based methods in single-resolution registration.

Reduces memory usage by a factor of five in multi-resolution registration.

Improves inference speed through latent code prediction.

Abstract

This work proposes NePhi, a generalizable neural deformation model which results in approximately diffeomorphic transformations. In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity. Specifically, NePhi 1) requires less memory compared to voxel-based learning approaches, 2) improves inference speed by predicting latent codes, compared to current existing neural deformation based registration approaches that \emph{only} rely on optimization, 3) improves accuracy via instance optimization, and 4) shows excellent deformation regularity which is highly desirable for medical image registration. We demonstrate the performance of NePhi on a 2D synthetic dataset as well as for real 3D medical image datasets (e.g., lungs and brains). Our results show that NePhi can match the accuracy of voxel-based representations in a single-resolution registration setting. For multi-resolution registration, our method matches the accuracy of current SOTA learning-based registration approaches with instance optimization while reducing memory requirements by a factor of five. Our code is available at https://github.com/uncbiag/NePhi.