学习术前磁共振和术中超声的二维关键点匹配。

Hassan Rasheed, Reuben Dorent, Maximilian Fehrentz, Daniil Morozov, Tina Kapur, William M Wells, Alexandra Golby, Sarah Frisken, Julia A Schnabel, Nazim Haouchine
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引用次数: 0

摘要

本文提出了一种纹理不变的二维关键点描述符,专门用于匹配术前磁共振(MR)图像和术中超声(US)图像。我们引入了一种匹配合成策略,其中术中US图像从考虑多种MR模式和术中US可变性的MR图像合成。我们通过在所有图像上强制关键点定位来构建我们的训练集,然后训练一个特定于患者的描述符网络,该网络以监督对比的方式学习纹理不变的判别特征,从而产生鲁棒的关键点描述符。我们在真实情况下的实验表明了该方法的有效性,优于目前最先进的方法,平均匹配精度达到80.35%。
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Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.

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.
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