DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
Journal
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Series/Report No.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Start Page
15682-15692
Date Issued
2025-01-01
Author(s)
Wu, Chun-Hung
Chen, Shih-Hong
Hu, Chih-Yao
Wu, Hsin-Yu
Chen, Kai-Hsin
Chen, Yu-You
Su, Chih-Hai
Liu, Yu-Lun
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.
Publisher
IEEE
Type
conference paper