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Found 4 papers, 2 papers with code
Restoring Connectivity in Vascular Segmentation using a Learned Post-Processing Model
Accurate segmentation of vascular networks is essential for computer-aided tools designed to address cardiovascular diseases.
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.
Strategies for Training Stain Invariant CNNs
By training the network on one commonly used staining modality and applying it to images that include corresponding but differently stained tissue structures, the presented unsupervised strategies demonstrate significant improvements over standard training strategies.
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