This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray videos without annotated ground truth. DeNVeR uses optical flow and layer separation, enhancing segmentation accuracy and adaptability through test-time training. A key component of our research is the introduction of the XACV dataset, the first X-ray angiography coronary video dataset with high-quality, manually labeled segmentation ground truth. Our evaluation demonstrates that DeNVeR outperforms current state-of-the-art methods in vessel segmentation. This paper marks an advance in medical imaging, providing a robust, data-efficient tool for disease diagnosis and treatment planning and setting a new standard for future research in video vessel segmentation. See our project page for video results at https://kirito878.github.io/DeNVeR/.

翻译：本文提出可变形神经血管表示（DeNVeR），这是一种无需标注真实值的X射线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。本研究的关键贡献是引入了XACV数据集——首个包含高质量人工标注分割真实值的X射线血管造影冠状动脉视频数据集。评估结果表明，DeNVeR在血管分割任务上优于当前最先进方法。本文标志着医学影像领域的进步，为疾病诊断与治疗规划提供了鲁棒且数据高效的工具，并为视频血管分割的未来研究设立了新标准。视频结果请访问项目页面：https://kirito878.github.io/DeNVeR/。