ReliMorph improves 3D deformable image registration with reliability guidance
Learning Where to Trust: Reliability-Guided Expert Routing and Velocity Gating for 3D Deformable Registration
Artificial IntelligenceComputer Vision and Pattern Recognition
Summary
Matching images that show soft tissues or organs in 3D is tricky because some parts don’t have clear details, making it hard to line them up correctly. The authors introduce ReliMorph, a system that assesses how reliable each local match is and uses that information to decide where and how to update the 3D alignment step-by-step. By focusing more on trustworthy areas and avoiding uncertain ones, ReliMorph reduces errors from ambiguous matches. Their tests show this approach works better than existing methods for various types of medical images.
What this means in practice
- •For medical image analysts: Improve the accuracy of aligning 3D medical scans for diagnosis and treatment planning by selectively trusting reliable image regions during registration.
- •For clinical software developers: Enhance deformable registration modules in imaging software to handle challenging cases with weak textures or severe deformations through reliability-guided updates.
Authors
Chenyu Guo, Wenjie Dai, Yiqian Chang, Zhiyuan Zhang, Yunfan Wei, Shuhan Qi
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 strength.Local 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.