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Found 3 papers, 2 papers with code
Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning
Based on these experiments, we provide guidelines for the annotation and training of cerebrovascular segmentation models.
Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction
To overcome these limitations, the clDice loss, a topological loss that focuses on the vessel centerlines, has been recently proposed.
Modeling and hexahedral meshing of cerebral arterial networks from centerlines
We proposed a vessel model based on penalized splines to overcome the limitations inherent to the centerline representation, such as noise and sparsity.
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