Background and Objective: The lack of benchmark datasets has impeded the development of slice-to-volume registration algorithms. Such datasets are difficult to annotate, primarily due to the dimensional difference within data and the dearth of task-specific software. We aim to develop a user-friendly tool to streamline dataset annotation for slice-to-volume registration. Methods: The proposed tool, named SVRDA, is an installation-free web application for platform-agnostic collaborative dataset annotation. It enables efficient transformation manipulation via keyboard shortcuts and smooth case transitions with auto-saving. SVRDA supports configuration-based data loading and adheres to the separation of concerns, offering great flexibility and extensibility for future research. Various supplementary features have been implemented to facilitate slice-to-volume registration. Results: We validated the effectiveness of SVRDA by indirectly evaluating the post-registration segmentation quality on UK Biobank data, observing a dramatic overall improvement (24.02% in the Dice Similarity Coefficient and 48.93% in the 95th percentile Hausdorff distance, respectively) supported by highly statistically significant evidence ($p<0.001$).We further showcased the clinical usage of SVRDA by integrating it into test-retest T1 quantification on in-house magnetic resonance images, leading to more consistent results after registration. Conclusions: SVRDA can facilitate collaborative annotation of benchmark datasets while being potentially applicable to other pipelines incorporating slice-to-volume registration. Full source code and documentation are available at https://github.com/Roldbach/SVRDA

翻译：背景与目标：基准数据集的缺乏阻碍了切片到体积配准算法的发展。此类数据集难以标注，主要源于数据间的维度差异以及缺乏专用软件。我们旨在开发一种用户友好型工具，以简化切片到体积配准的数据集标注流程。方法：所提出的工具名为SVRDA，是一款无需安装的Web应用程序，支持跨平台协作式数据集标注。该工具通过键盘快捷键实现高效变换操作，并借助自动保存功能平滑切换病例。SVRDA支持基于配置的数据加载，并遵循关注点分离原则，为未来研究提供了极大的灵活性与可扩展性。此外，我们实现了多种辅助功能以促进切片到体积配准。结果：我们通过间接评估英国生物银行数据配准后的分割质量，验证了SVRDA的有效性，观察到整体性能显著提升（Dice相似系数提升24.02%，95%分位数豪斯多夫距离改善48.93%），且具有高度统计学显著性证据（$p<0.001$）。我们进一步通过将SVRDA集成到基于院内磁共振图像的测试-重测T1定量分析中，展示了其临床用途，配准后结果一致性更高。结论：SVRDA可促进基准数据集的协作式标注，同时具备潜在适用性于其他包含切片到体积配准的流程。完整源代码和文档可在https://github.com/Roldbach/SVRDA获取。