MedDIFT: Multi-Scale Diffusion-Based Correspondence in 3D Medical Imaging

• URL: http://arxiv.org/abs/2512.05571v1
• Date: Fri, 05 Dec 2025 09:53:07 GMT
• Title: MedDIFT: Multi-Scale Diffusion-Based Correspondence in 3D Medical Imaging
• Authors: Xingyu Zhang, Anna Reithmeir, Fryderyk Kögl, Rickmer Braren, Julia A. Schnabel, Daniel M. Lang,
• Abstract summary: We present MedDIFT, a training-free 3D correspondence framework that leverages multi-scale features from a pretrained latent medical diffusion model as voxel descriptors.<n>On a publicly available lung CT dataset, MedDIFT achieves correspondence accuracy comparable to the state-of-the-art UniGradICON model.
• Score: 6.520674045578402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate spatial correspondence between medical images is essential for longitudinal analysis, lesion tracking, and image-guided interventions. Medical image registration methods rely on local intensity-based similarity measures, which fail to capture global semantic structure and often yield mismatches in low-contrast or anatomically variable regions. Recent advances in diffusion models suggest that their intermediate representations encode rich geometric and semantic information. We present MedDIFT, a training-free 3D correspondence framework that leverages multi-scale features from a pretrained latent medical diffusion model as voxel descriptors. MedDIFT fuses diffusion activations into rich voxel-wise descriptors and matches them via cosine similarity, with an optional local-search prior. On a publicly available lung CT dataset, MedDIFT achieves correspondence accuracy comparable to the state-of-the-art learning-based UniGradICON model and surpasses conventional B-spline-based registration, without requiring any task-specific model training. Ablation experiments confirm that multi-level feature fusion and modest diffusion noise improve performance.

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