Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration

arXiv:2609.15669 · cs.CV, cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: Preprint Under Review

Code: https://github.com/InsightSoftwareConsortium/ITKElastix

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 33× in this paper · explained in 2 episodes
• Alignment — 28× in this paper · explained in 1 episode
• Accuracy — 27× in this paper · explained in 1 episode
• Consistency — 25× in this paper · explained in 3 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Benchmarking — 8× in this paper · explained in 4 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Dice Similarity Coefficient — 4× in this paper
• Foundation Models — 3× in this paper · explained in 7 episodes
• Human Connectome Project — 3× in this paper
• Magnetic Resonance Imaging — 3× in this paper
• Modality Gap — 3× in this paper · explained in 4 episodes
• Computed Tomography — 2× in this paper
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• International Symposium on Biomedical Imaging — 2× in this paper
• Multimodal Dataset — 2× in this paper · explained in 2 episodes
• Mutual Information — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Model — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Homogenization — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• SAMIR, an efficient registration framework via robust feature learning from SAM
• ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
• MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training
• Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models