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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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Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound

  • 会议论文

  • 发表日期：

    2025年

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  • Hassan Rasheed,
  • Reuben Dorent,
  • Maximilian Fehrentz,
  • Tina Kapur,
  • William M. Wells,
  • Alexandra Golby,
  • Sarah Frisken,
  • Julia A. Schnabel,
  • Nazim Haouchine

摘要

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.