bibtext: @InProceedings{ Zha_MemWarp_MICCAI2024,
                   author = { Zhang, Hang and Chen, Xiang and Hu, Renjiu and Liu, Dongdong and Li, Gaolei and Wang, Rongguang },
                   title = { { MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
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 \textit{MemWarp}, a learning framework that leverages a memory network to store prototypical information tailored to different anatomical regions. 
  \textit{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 \url{https://github.com/tinymilky/Mem-Warp}.
title: MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters
authors: Zhang, Hang Chen, Xiang Hu, Renjiu Liu, Dongdong Li, Gaolei Wang, Rongguang