DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation

• URL: http://arxiv.org/abs/2406.01591v3
• Date: Sat, 07 Dec 2024 18:27:23 GMT
• Title: DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
• Authors: 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,
• Abstract summary: Deformable Neural Vessel Representations (DeNVeR) is an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth.<n>Key contributions include a novel layer bootstrapping technique, a parallel vessel motion loss, and the integration of Eulerian motion fields for modeling complex vessel dynamics.
• Score: 3.1977656204331684
• License: http://creativecommons.org/licenses/by/4.0/
• 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. See our project page for video results at https://kirito878.github.io/DeNVeR/.

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