DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation

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

Keywords: Video vessel segmentation, Unsupervised learning, X-ray angiography videos dataset

Abstract: This paper presents **De**formable **N**eural **Ve**ssel **R**epresentations (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. This work advances medical imaging by providing a robust, data-efficient tool for vessel segmentation. It sets a new standard for video-based vessel segmentation research, offering greater flexibility and potential for clinical applications.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 892