用扩散模型直接去噪形变场，实现可观察可调整的医学图像配准。
DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image Registration
- 直接对形变场进行迭代去噪，提升配准过程透明度。
- 在ACDC数据集上Dice得分比现有方法高1.32。
- 采样过程支持实时观察与手动干预，适合临床交互场景。
可变形图像配准旨在精确对齐不同模态或时间的医学图像。传统深度学习方法虽有效，但常缺乏可解释性、推理过程中的实时可观测性及可调性。去噪扩散模型通过将配准重构为迭代去噪过程提供了新路径，但现有方法未充分利用采样阶段，丧失了推理过程中的连续可观测性。为此，我们提出DiffuseReg，一种基于扩散模型的新方法，其核心是直接对形变场而非图像进行去噪，以增强透明性。我们设计了一种基于Swin Transformer的新型去噪网络，能更好融合移动图像与固定图像，并在去噪过程中整合扩散时间步信息。此外，引入相似性一致性正则化以增强对去噪过程的控制。在ACDC数据集上的实验表明，DiffuseReg在Dice分数上优于现有扩散配准方法1.32分。其采样过程实现了前所未有的实时输出可观测性与可调性，超越以往深度模型。
原文摘要 · Abstract (English)
Deformable image registration aims to precisely align medical images from different modalities or times. Traditional deep learning methods, while effective, often lack interpretability, real-time observability and adjustment capacity during registration inference. Denoising diffusion models present an alternative by reformulating registration as iterative image denoising. However, existing diffusion registration approaches do not fully harness capabilities, neglecting the critical sampling phase that enables continuous observability during the inference. Hence, we introduce DiffuseReg, an innovative diffusion-based method that denoises deformation fields instead of images for improved transparency. We also propose a novel denoising network upon Swin Transformer, which better integrates moving and fixed images with diffusion time step throughout the denoising process. Furthermore, we enhance control over the denoising registration process with a novel similarity consistency regularization. Experiments on ACDC datasets demonstrate DiffuseReg outperforms existing diffusion registration methods by 1.32 in Dice score. The sampling process in DiffuseReg enables real-time output observability and adjustment unmatched by previous deep models.
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