arXiv cs.AI· Chenyu Guo, Wenjie Dai, Yiqian Chang, Zhiyuan Zhang, Yunfan Wei, Shuhan Qi·· 13 小时前AI 评分20
ReliMorph：以可靠性引导专家路由与速度门控的 3D 可变形配准框架
Learning Where to Trust: Reliability-Guided Expert Routing and Velocity Gating for 3D Deformable Registration
AI 导读
ReliMorph 是一个可靠性路由的对称 3D 可变形配准框架，将对应关系评估与空间上下文选择、形变更新强度耦合。它用相关分布刻画对应歧义，并以此引导空间专家融合、在由粗到细的细化中门控残差速度，反向不一致反馈进一步指导更细金字塔层的路由与门控。作者称这是首个将空间专家路由与残差速度门控同时以对应可靠性为条件的 3D 可变形配准框架，实验在解剖重叠度上稳定优于对比方法。
正文 · 原文
Abstract:Deformable image registration relies on accurate anatomical correspondences. However, explicitly coupling local matching reliability with spatial context selection and deformation update strength remains insufficiently explored in learning-based registration. This challenge is particularly pronounced in homogeneous, weakly textured, and severely deformed regions, where ambiguous matches may induce erroneous deformation updates. We propose ReliMorph, a reliability-routed symmetric framework for 3D deformable registration that couples correspondence assessment with spatial context selection and update this http URL correlation distributions characterize correspondence ambiguity using shared anatomical features with modality-specific adaptation. These reliability cues guide spatial expert fusion and gate residual velocities during coarse-to-fine refinement. Updated inverse-inconsistency feedback guides routing and gating at finer pyramid levels. To our knowledge, ReliMorph is the first 3D deformable registration framework to jointly condition spatial expert routing and residual velocity gating on correspondence reliability. Extensive experiments demonstrate that ReliMorph consistently outperforms competing methods in anatomical overlap, highlighting its effectiveness across unimodal and multimodal registration tasks.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.36672 [cs.AI] |
| (or arXiv:2610.36672v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36672 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chenyu Guo [view email]
[v1]
Tue, 27 Oct 2026 05:43:45 UTC (9,286 KB)
来源：arXiv cs.AI · arxiv.org