MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters

TL;DR

MemWarp introduces a novel learning framework with a memory network that preserves anatomical discontinuities in cardiac image registration, outperforming existing methods without requiring segmentation masks during inference.

Contribution

The paper presents MemWarp, a new approach that decouples feature extraction from similarity matching and uses a memory network to handle local discontinuities in cardiac registration.

Findings

Achieves 7.1% Dice score improvement over state-of-the-art methods.

Effectively preserves local discontinuities without segmentation masks.

Outperforms existing methods in registration accuracy and realistic deformations.

Abstract

Many existing learning-based deformable image registration methods impose constraints on deformation fields to ensure they are globally smooth and continuous. However, this assumption does not hold in cardiac image registration, where different anatomical regions exhibit asymmetric motions during respiration and movements due to sliding organs within the chest. Consequently, such global constraints fail to accommodate local discontinuities across organ boundaries, potentially resulting in erroneous and unrealistic displacement fields. In this paper, we address this issue with MemWarp, a learning framework that leverages a memory network to store prototypical information tailored to different anatomical regions. MemWarp is different from earlier approaches in two main aspects: firstly, by decoupling feature extraction from similarity matching in moving and fixed images, it facilitates more effective utilization of feature maps; secondly, despite its capability to preserve discontinuities, it eliminates the need for segmentation masks during model inference. In experiments on a publicly available cardiac dataset, our method achieves considerable improvements in registration accuracy and producing realistic deformations, outperforming state-of-the-art methods with a remarkable 7.1\% Dice score improvement over the runner-up semi-supervised method. Source code will be available at https://github.com/tinymilky/Mem-Warp.