DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
It addresses vessel segmentation for medical imaging, specifically in X-ray angiography, with incremental improvements through novel techniques.
This paper tackles unsupervised vessel segmentation in X-ray angiography videos by introducing DeNVeR, which uses optical flow and layer separation with test-time training, resulting in outperforming state-of-the-art methods in accuracy and generalization on datasets like XACV and CADICA.
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.