发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出递归不确定性门控图像配准（RUGI），通过迭代细化形变场并利用空间门控聚焦难配准区域，在心脏MRI和超声心动图数据集上显著提升配准精度，且可无修改应用于现有模型。
AI 中文摘要
传统的图像配准算法对域偏移具有鲁棒性且误差低，但速度慢且计算成本高。深度学习方法在推理时高效，但在域外样本上面临挑战。我们提出了递归不确定性门控图像配准（RUGI），一种用于迭代细化基于学习的配准模型预测的形变场的算法。在每次迭代中，配准模型预测一个增量形变，并由门控图调节更新。因此，细化集中在仍然难以配准的区域。我们探索了两种门控策略：一种基于学习的不确定性方法和一种图像残差误差方法。我们在心脏MRI和超声心动图数据集上评估了RUGI，并显示出相对于单步推理的一致改进。消融实验表明，仅迭代细化就能改善配准，但信息丰富的空间门控提供了显著的额外收益。RUGI的误差门控变体也可以直接应用于现有的预训练模型；应用于VoxelMorph、TransMorph和CycleMorph时，在不修改原始训练过程的情况下，MSE降低了27-37%。配准性能的改进反映在射血分数估计相对于真实值的误差减少上。这些结果表明，空间选择性迭代细化提供了一种在推理时提高配准精度的有效策略。
英文摘要
Conventional image registration algorithms are robust to domain shifts and achieve low errors, but they are slow and computationally expensive. Deep-learning methods are efficient at inference-time, but face challenges in out-of-domain samples. We propose Recursive Uncertainty-Gated Image Registration (RUGI), an algorithm for iteratively refining deformation fields predicted by learning-based registration models. At each iteration, the registration model predicts an incremental deformation, and a gating map modulates the update. Refinements are hence concentrated in regions that remain difficult to register. We explore two gating strategies: a learned uncertainty-based approach and an image residual error approach. We evaluate RUGI on cardiac MRI and echocardiography datasets and show consistent improvements over single-step inference. Ablation experiments demonstrate that iterative refinement alone improves registration, but informative spatial gating provides a significant additional benefit. The error-gated variant of RUGI can also be applied directly to existing pretrained models; applied to VoxelMorph, TransMorph, and CycleMorph, it yields MSE reductions of 27-37% with no modification to the original training procedure. The improvements in registration performance are reflected in decreased errors in ejection fraction estimation relative to ground truths. These results demonstrate that spatially selective iterative refinement provides an effective strategy to improve registration accuracy at inference-time.
