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

MCML Authors

Anna Reithmeir

→ Group Julia Schnabel
Computational Imaging and AI in Medicine

Fryderyk Kögl

→ Group Julia Schnabel
Computational Imaging and AI in Medicine

Julia Schnabel

Prof. Dr.

Principal Investigator

Computational Imaging and AI in Medicine

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.

misc ZRK+25