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General Vision Encoder Features as Guidance in Medical Image Registration

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Julia Schnabel

Prof. Dr.

Principal Investigator

Computational Imaging and AI in Medicine

Abstract

General vision encoders like DINOv2 and SAM have recently transformed computer vision. Even though they are trained on natural images, such encoder models have excelled in medical imaging, e.g., in classification, segmentation, and registration. However, no in-depth comparison of different state-of-the-art general vision encoders for medical registration is available. In this work, we investigate how well general vision encoder features can be used in the dissimilarity metrics for medical image registration. We explore two encoders that were trained on natural images as well as one that was fine-tuned on medical data. We apply the features within the well-established B-spline FFD registration framework. In extensive experiments on cardiac cine MRI data, we find that using features as additional guidance for conventional metrics improves the registration quality.

inproceedings

WBIR @MICCAI 2024

11th International Workshop on Biomedical Image Registration at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. Marrakesh, Morocco, Oct 06-10, 2024.

Authors

F. Kögl • A. Reithmeir • V. Sideri-Lampretsa • I. Machado • R. Braren • D. Rückert • J. A. Schnabel • V. A. Zimmer

Links

DOI URL

Research Area

 C1 | Medicine

BibTeXKey: KRS+24b

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