# MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters

arXiv: https://arxiv.org/abs/2407.08093 · HTML: https://mlsanity.com/paper/2407.08093/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?](https://mlsanity.com/paper/2208.04939/) (2022)
- [Spatially Covariant Image Registration with Text Prompts](https://mlsanity.com/paper/2311.15607/) (2023)
- [A Deep Discontinuity-Preserving Image Registration Network](https://mlsanity.com/paper/2107.04440/) (2021)
- [Spatially Covariant Lesion Segmentation](https://mlsanity.com/paper/2301.07895/) (2023)

## Built on by (most cited first, 13 of 13)

- [Beyond the LUMIR challenge: The pathway to foundational registration models](https://mlsanity.com/paper/2505.24160/) (2025)
- [Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization](https://mlsanity.com/paper/2409.05040/) (2024)
- [Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing](https://mlsanity.com/paper/2506.10813/) (2025)
- [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025)
- [Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization](https://mlsanity.com/paper/2602.06288/) (2026)
- [VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration](https://mlsanity.com/paper/2506.19975/) (2025)
- [SAMIR, an efficient registration framework via robust feature learning from SAM](https://mlsanity.com/paper/2509.13629/) (2025)
- [Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024](https://mlsanity.com/paper/2409.00917/) (2024)
- [Gaussian Primitive Optimized Deformable Retinal Image Registration](https://mlsanity.com/paper/2508.16852/) (2025)
- [Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge](https://mlsanity.com/paper/2410.20812/) (2024)
- [Ideal Registration? Segmentation is All You Need](https://mlsanity.com/paper/2509.15784/) (2025)
- [FMIR, a foundation model-based Image Registration Framework for Robust Image Registration](https://mlsanity.com/paper/2601.17529/) (2026)
- [Reg-TTR, Test-Time Refinement for Fast, Robust and Accurate Image Registration](https://mlsanity.com/paper/2601.19114/) (2026)

## Abstract (arXiv)

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.