端到端学习脊柱手术中无需标记的精准配准方法
End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery
- 联合优化分割与配准，无需人工标注分割标签
- 比现有方法降低32%目标误差，61%均方根误差
- 适合追求全自动、无创术中导航的临床研究者
脊柱手术术中导航需达到毫米级精度。目前依赖辐射性强的术中成像和骨锚定标记，具有侵入性且干扰手术流程。无标记的RGB-D配准方法是潜在替代方案。然而现有方法使用弱分割标签分离解剖结构，可能在配准过程中传播误差。本文提出End2Reg，一种端到端深度学习框架，联合优化分割与配准，无需分割标签和手动步骤。网络学习针对配准任务的特定分割掩码，仅通过配准目标进行优化，无需显式分割监督。在体外和体内基准测试中，End2Reg实现最先进性能，将中位目标注册误差降低32%，均方根误差降低61%，并在部分遮挡下保持鲁棒性。消融实验表明端到端优化显著提升配准精度。总体上，End2Reg推动了完全自动、无标记术中导航的发展。代码与交互可视化见：https://lorenzopettinari.github.io/end-2-reg/
原文摘要 · Abstract (English)
Intraoperative navigation in spine surgery demands millimeter-level accuracy. Currently, this is achieved through radiation-intensive intraoperative imaging and bone-anchored markers that are invasive and disrupt surgical workflow. Markerless RGB-D registration methods offer a promising alternative. However, existing approaches rely on weak segmentation labels to isolate relevant anatomical structures, potentially propagating errors through the registration process. We present End2Reg, an end-to-end deep learning framework that jointly optimizes segmentation and registration, eliminating the need for segmentation labels and manual steps. The network learns task-specific segmentation masks optimized for registration, guided solely by the registration objective without explicit segmentation supervision. End2Reg achieves state-of-the-art performance on ex- and in-vivo benchmarks, reducing median Target Registration Error by 32% and mean Root Mean Square Error by 61%, while maintaining robust performance under partial occlusions. Ablation results confirm that end-to-end optimization significantly improves registration accuracy. Overall, End2Reg advances towards fully automatic, markerless intraoperative navigation. Code and interactive visualizations are available at: https://lorenzopettinari.github.io/end-2-reg/.
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。