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Rasheed, H. ; Dorent, R.* ; Fehrentz, M.* ; Kapur, T.* ; Wells, W.M.* ; Golby, A.* ; Frisken, S.* ; Schnabel, J.A. ; Haouchine, N.*

Learning to match 2D keypoints across preoperative MR and intraoperative ultrasound.

In: (Simplifying Medical Ultrasound). Berlin [u.a.]: Springer, 2025. 78-87 (Lect. Notes Comput. Sc. ; 15186 LNCS)
DOI
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Simplifying Medical Ultrasound
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15186 LNCS, Heft: , Seiten: 78-87 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)