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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01591 |
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| _version_ | 1866913755367997440 |
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| author | Wu, Chun-Hung Chen, Shih-Hong Hu, Chih-Yao Wu, Hsin-Yu Chen, Kai-Hsin Chen, Yu-You Su, Chih-Hai Lee, Chih-Kuo Liu, Yu-Lun |
| author_facet | Wu, Chun-Hung Chen, Shih-Hong Hu, Chih-Yao Wu, Hsin-Yu Chen, Kai-Hsin Chen, Yu-You Su, Chih-Hai Lee, Chih-Kuo Liu, Yu-Lun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation Wu, Chun-Hung Chen, Shih-Hong Hu, Chih-Yao Wu, Hsin-Yu Chen, Kai-Hsin Chen, Yu-You Su, Chih-Hai Lee, Chih-Kuo Liu, Yu-Lun Computer Vision and Pattern Recognition 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. |
| title | DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.01591 |