DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
<|reference_start|>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 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/.<|reference_end|>
@article{wu2024denver:,
    title={DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video
  Vessel Segmentation},
    author={Chun-Hung Wu, Shih-Hong Chen, Chih-Yao Hu, Hsin-Yu Wu, Kai-Hsin Chen,
  Yu-You Chen, Chih-Hai Su, Chih-Kuo Lee, Yu-Lun Liu},
    journal={arXiv preprint arXiv:2406.01591},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01591},
    primaryClass={cs.CV}
}