Effective Feature Learning for 3D Medical Registration via Domain-Specialized DINO Pretraining
Medical image registration is a critical component of clinical imaging workflows, enabling accurate longitudinal assessment, multi-modal data fusion, and image-guided interventions. Intensity-based approaches often struggle with interscanner variability and complex anatomical deformations, whereas feature-based methods offer improved robustness by leveraging semantically informed representations. In this work, we investigate DINO-style self-supervised pretraining directly on 3D medical imaging data, aiming to learn dense volumetric features well suited for deformable registration. We assess the resulting representations on challenging interpatient abdominal registration task across both M...
| Authors | Eytan Kats, Mattias P. Heinrich |
|---|---|
| Primary category | cs.CV |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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