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Found 6 papers, 1 papers with code
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
To this end, we propose a combination of a global controller leveraging voxel mask segmentations to provide boundary conditions for vessels of interest to a local, iterative vessel segmentation model.
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Hence, SIRE can be trained with arbitrarily oriented vessels with varying radii to generalise to vessels with a wide range of calibres and tortuosity.
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
The application of deep learning models to large-scale data sets requires means for automatic quality assurance.
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
We represent the AAA wall over time as the zero-level set of a signed distance function (SDF), estimated by a multilayer perception that operates on space and time.
Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
This allows us to model complex vascular structures with a representation that is implicit, continuous, light-weight, and easy to integrate with deep learning algorithms.
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis.
