DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation

Autor:	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
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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/. Comment: Project page: https://kirito878.github.io/DeNVeR/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.01591 Zobrazit plný text záznamu View this record from Arxiv