  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/.
