Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction

arXiv:2307.11603v2 Announce Type: replace-cross
Abstract: Vessel segmentation and centerline extraction are two crucial preliminary tasks for many computer-aided diagnosis tools dealing with vascular diseases. Recently, deep-learning based methods have been widely applied to these tasks. However, classic deep-learning approaches struggle to capture the complex geometry and specific topology of vascular networks, which is of the utmost importance in most applications. To overcome these limitations, the clDice loss, a topological loss that focuses on the vessel centerlines, has been recently proposed. …

abstract arxiv computer cs.cv diagnosis diseases eess.iv extraction geometry loss segmentation struggle tasks tools topology type