Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration
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Multimodal image registration is a key component of many clinical workflows, yet it remains challenging because corresponding anatomical structures often exhibit substantially different image intensit...
Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted modality-robust descriptors such as Modality-Inde...
arXiv:2610.34600v1 Announce Type: new Abstract: Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted...
UniReg is a conditional unified model for medical image registration that adapts deformation field estimation based on anatomical priors, registration type constraints, and instance-specific features. It combines the precision of task‑specific learning with the generalization of traditional optimization, enabling effective alignment across diverse CT and MR scenarios within a single framework. Experiments show UniReg outperforms state‑of‑the‑art learning‑based methods in accuracy while providing strong cross‑scenario generalization and reducing training cost and model redundancy.
arXiv:2607. 23343v1 Announce Type: cross Abstract: Intraoperative 2D/3D registration aligns preoperative CT volumes with intraoperative X-ray or fluoroscopic images and is essential for image-guided interventions.
ReliMorph is a novel 3D deformable registration framework that integrates correspondence reliability with spatial expert routing and residual velocity gating. By using local correlation distributions to assess match ambiguity, the method guides expert fusion and velocity updates during coarse‑to‑fine refinement, with inverse‑inconsistency feedback refining decisions at finer levels. Experiments show that ReliMorph consistently improves anatomical overlap over competing methods in both unimodal and multimodal registration tasks.