This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth. DeNVeR utilizes optical flow and layer separation techniques, enhancing segmentation accuracy and adaptability through test-time training. Key contributions include a novel layer separation bootstrapping technique, a parallel vessel motion loss, and the integration of Eulerian motion fields for modeling complex vessel dynamics. A significant component of this research is the introduction of the XACV dataset, the first X-ray angiography coronary video dataset with high-quality, manually labeled segmentation ground truth. Extensive evaluations on both XACV and CADICA datasets demonstrate that DeNVeR outperforms current state-of-the-art methods in vessel segmentation accuracy and generalization capability while maintaining temporal coherency.

翻译：本文提出可变形神经血管表示（DeNVeR），一种无需标注真值的X射线血管造影视频血管分割无监督方法。DeNVeR利用光流与层分离技术，通过测试时训练提升分割精度与适应性。核心贡献包括：新颖的层分离自举技术、并行血管运动损失函数，以及用于建模复杂血管动力学的欧拉运动场集成。本研究的重要部分是首次引入XACV数据集——首个包含高质量人工标注分割真值的X射线血管造影冠状动脉视频数据集。在XACV与CADICA数据集上的大量实验表明，DeNVeR在血管分割精度与泛化能力上均优于当前最先进方法，同时保持了时序一致性。