DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation

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.

@article{wu2025_2406.01591,
  title={ DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation },
  author={ Chun-Hung Wu and Shih-Hong Chen and Chih-Yao Hu and Hsin-Yu Wu and Kai-Hsin Chen and Yu-You Chen and Chih-Hai Su and Chih-Kuo Lee and Yu-Lun Liu },
  journal={arXiv preprint arXiv:2406.01591},
  year={ 2025 }
}