跨模态医学图像配准，精准匹配术前核磁与术中超声关键点
Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
- 通过合成术中超声图像训练纹理无关的关键点描述符
- 在真实病例上实现80.35%的平均匹配精度，优于现有方法
- 适合需要术中实时导航的神经外科和微创手术场景
本文提出一种专为匹配术前磁共振（MR）图像与术中超声（US）图像设计的纹理无关2D关键点描述符。采用基于合成的匹配策略，从多模态MR图像生成考虑术中超声变化的合成超声图像。通过在所有图像上强制关键点定位构建训练集，并训练患者特异性描述符网络，以监督对比方式学习纹理无关的判别特征，从而获得鲁棒的关键点描述符。在真实病例与真实标注数据上的实验表明，该方法有效，优于当前最先进方法，在平均匹配精度上达到80.35%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。