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
this https URL

本文提出了一种无监督的 Deformable Neural Vessel Representations (DeNVeR) 方法，用于 X 射线视频中的血管分割，通过光流和层分离增强分割精度和适应性，并引入了首个高质量手动标记分割地面真实性的 X 射线血管造影冠状视频数据集 XACV，评估结果表明 DeNVeR 在血管分割方面优于当前最先进的方法，为医学成像领域提供了一个强大的高效工具，为疾病诊断和治疗规划提供支持，并为未来视频血管分割研究树立了新的标准。详情请参阅项目页面中的视频结果。