1. DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
- Author
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Wu, Chun-Hung, Chen, Shih-Hong, Hu, Chih-Yao, Wu, Hsin-Yu, Chen, Kai-Hsin, Chen, Yu-You, Su, Chih-Hai, Lee, Chih-Kuo, and Liu, Yu-Lun
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
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/., Comment: Project page: https://kirito878.github.io/DeNVeR/
- Published
- 2024