用跨模态逆向神经渲染实现术中脑手术3D/2D配准，精度更高。
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
- 分离解剖结构与术中外观，通过超网络控制神经辐射场外观
- 在临床数据上达到优于现有方法的配准精度，符合临床标准
- 适合需要高精度术中导航的神经外科医生使用
本文提出一种基于跨模态逆向神经渲染的新型3D/2D术中配准方法，用于神经外科手术。该方法将隐式神经表示分为两部分：术前处理解剖结构，术中建模外观。通过多风格超网络控制神经辐射场（Neural Radiance Field）的外观实现解耦。训练完成后，隐式神经表示作为可微分渲染引擎，通过最小化渲染图像与术中目标图像的差异来估计手术相机位姿。我们在回顾性临床病例数据上测试了该方法，结果表明其性能优于当前最先进方法，并满足现有临床配准标准。代码与补充资源见https://maxfehrentz.github.io/style-ngp/。
原文摘要 · Abstract (English)
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/.
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。