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General vision encoder features as guidance in medical image registration.
In: (Biomedical Image Registration). Berlin [u.a.]: Springer, 2024. 265-279 (Lect. Notes Comput. Sc. ; 15249 LNCS)
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. The code is available at github.com/compai-lab/2024-miccai-koegl.
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Feature-based Distance Measures ; Foundation Models
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
Biomedical Image Registration
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 15249 LNCS,
Seiten: 265-279
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)