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arXiv · 2512.05571

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

Xingyu Zhang·Anna Reithmeir·Fryderyk Kögl·Rickmer Braren·Julia A. Schnabel·Daniel M. Lang
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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 shows promising capability in identifying anatomical correspondence without requiring any task-specific model training. Ablation experiments confirm that multi-level feature fusion and modest diffusion noise improve performance. Code is available online.

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Xingyu Zhang, Anna Reithmeir, Fryderyk Kögl, Rickmer Braren, Julia A. Schnabel, Daniel M. Lang. 2026-02-23. MedDIFT: Multi-Scale Diffusion-Based Correspondence in 3D Medical Imaging. https://arxiv.org/abs/2512.05571

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