MemWarp：借助记忆化解剖滤波器的保不连续性心脏配准方法
MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters
AI总结：
针对现有基于学习的心脏图像配准方法全局平滑约束无法适配器官边界局部不连续的问题，提出MemWarp框架，通过记忆网络存储解剖区域原型信息，解耦特征提取与匹配，推理无需分割掩码，在公开数据集上配准精度显著优于现有最优方法。
摘要：
现有许多基于学习的可变形图像配准方法会对形变场施加约束，以保证其全局平滑连续。但这一假设在心脏图像配准中并不成立：由于胸腔内器官存在滑动，不同解剖区域在呼吸和运动过程中会呈现不对称运动。因此这类全局约束无法适配器官边界处的局部不连续性，可能导致错误且不符合实际的位移场。本文提出MemWarp学习框架来解决该问题，该框架利用记忆网络存储适配不同解剖区域的原型信息。MemWarp与早期方法主要有两点不同：第一，它将浮动图像与固定图像的特征提取和相似度匹配解耦，提升了特征图的利用效率；第二，尽管具备保留不连续性的能力，模型推理时无需分割掩码。在一个公开心脏数据集上的实验中，我们的方法在配准精度和生成符合实际的形变方面均取得显著提升，性能优于现有最优方法，其中Dice分数较第二名的半监督方法高出7.1%。源代码将发布于https://github.com/tinymilky/Mem-Warp。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
