TY - GEN
T1 - Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
AU - Rasheed, Hassan
AU - Dorent, Reuben
AU - Fehrentz, Maximilian
AU - Kapur, Tina
AU - Wells, William M.
AU - Golby, Alexandra
AU - Frisken, Sarah
AU - Schnabel, Julia A.
AU - Haouchine, Nazim
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85206491077&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73647-6_8
DO - 10.1007/978-3-031-73647-6_8
M3 - Conference contribution
AN - SCOPUS:85206491077
SN - 9783031736469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 87
BT - Simplifying Medical Ultrasound - 5th International Workshop, ASMUS 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Gomez, Alberto
A2 - Khanal, Bishesh
A2 - King, Andrew
A2 - Namburete, Ana
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Workshop on Advances in Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
Y2 - 6 October 2024 through 6 October 2024
ER -