DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation

Abstract

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. Please see our project page at kirito878.github.io/DeNVeR.

Cite

Text

Wu et al. "DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.01462

Markdown

[Wu et al. "DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/wu2025cvpr-denver/) doi:10.1109/CVPR52734.2025.01462

BibTeX

@inproceedings{wu2025cvpr-denver,
  title     = {{DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation}},
  author    = {Wu, Chun-Hung and Chen, Shih-Hong and Hu, Chih-Yao and Wu, Hsin-Yu and Chen, Kai-Hsin and Chen, Yu-You and Su, Chih-Hai and Lee, Chih-Kuo and Liu, Yu-Lun},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2025},
  pages     = {15682-15692},
  doi       = {10.1109/CVPR52734.2025.01462},
  url       = {https://mlanthology.org/cvpr/2025/wu2025cvpr-denver/}
}