跨模态逆神经渲染的术中配准
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
AI总结：
本文提出基于跨模态逆神经渲染的神经外科术中配准方法，通过解耦隐式神经表示并利用超网络控制外观，以可微渲染优化相机姿态，在临床数据上优于现有方法并满足临床标准。
摘要：
本文提出了一种用于神经外科手术中三维/二维术中配准的新方法，该方法基于跨模态逆神经渲染。我们的方法将隐式神经表示分解为两个组成部分，分别处理术前解剖结构和术中外观。这种解耦通过使用多风格超网络控制神经辐射场的外观来实现。一旦训练完成，隐式神经表示便充当可微渲染引擎，通过最小化其渲染图像与目标术中图像之间的差异来估计手术相机姿态。我们在临床病例的回顾性患者数据上测试了我们的方法，结果表明，我们的方法在满足当前临床配准标准的同时，优于最先进的方法。代码和其他资源可在 https://maxfehrentz.github.io/style-ngp/ 获取。
英文摘要：
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. This disentanglement is achieved by controlling a Neural Radiance Field's appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration. Code and additional resources can be found at https://maxfehrentz.github.io/style-ngp/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
