提出iPEAR框架，提升医学影像变形配准精度与自适应迭代能力
iPEAR: Iterative Pyramid Estimation with Attention and Residuals for Deformable Medical Image Registration
- 采用注意力-残差融合模块动态缓解解码阶段形变误差累积
- 设计双阶段阈值控制迭代策略，按图像差异自适应调整优化次数
- 在脑MRI与腹部CT数据上超越当前最优模型，且推理速度不降
现有金字塔配准网络易积累解剖错位，且缺乏根据图像形变程度动态调整优化迭代次数的有效机制，导致性能下降。为此，我们提出iPEAR。具体而言，iPEAR采用提出的融合注意力-残差模块（FARM）进行解码，包含注意力路径与残差路径，以缓解解剖错位的累积。同时，我们提出双阶段阈值控制迭代（TCI）策略，通过评估配准稳定性和收敛性，自适应决定不同图像的优化迭代次数。在三个公开脑部MRI数据集和一个公开腹部CT数据集上的大量实验表明，iPEAR在精度上优于当前最优（SOTA）配准网络，同时保持相当的推理速度与模型参数量。泛化性与消融实验证实了FARM与TCI的有效性。
原文摘要 · Abstract (English)
Existing pyramid registration networks may accumulate anatomical misalignments and lack an effective mechanism to dynamically determine the number of optimization iterations under varying deformation requirements across images, leading to degraded performance. To solve these limitations, we propose iPEAR. Specifically, iPEAR adopts our proposed Fused Attention-Residual Module (FARM) for decoding, which comprises an attention pathway and a residual pathway to alleviate the accumulation of anatomical misalignment. We further propose a dual-stage Threshold-Controlled Iterative (TCI) strategy that adaptively determines the number of optimization iterations for varying images by evaluating registration stability and convergence. Extensive experiments on three public brain MRI datasets and one public abdomen CT dataset show that iPEAR outperforms state-of-the-art (SOTA) registration networks in terms of accuracy, while achieving on-par inference speed and model parameter size. Generalization and ablation studies further validate the effectiveness of the proposed FARM and TCI.
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