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

• URL: http://arxiv.org/abs/2307.11603v2
• Date: Thu, 22 Feb 2024 09:47:18 GMT
• Title: Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction
• Authors: Pierre Roug\'e, Nicolas Passat, Odyss\'ee Merveille
• Abstract summary: We propose to replace the soft-skeleton algorithm by a U-Net which computes the vascular skeleton directly from the segmentation. We build upon this network a cascaded U-Net trained with the clDice loss to embed topological constraints during the segmentation.
• Score: 2.264332709661011
• License: http://creativecommons.org/licenses/by/4.0/
• 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. This loss requires computing, with a proposed soft-skeleton algorithm, the skeletons of both the ground truth and the predicted segmentation. However, the soft-skeleton algorithm provides suboptimal results on 3D images, which makes the clDice hardly suitable on 3D images. In this paper, we propose to replace the soft-skeleton algorithm by a U-Net which computes the vascular skeleton directly from the segmentation. We show that our method provides more accurate skeletons than the soft-skeleton algorithm. We then build upon this network a cascaded U-Net trained with the clDice loss to embed topological constraints during the segmentation. The resulting model is able to predict both the vessel segmentation and centerlines with a more accurate topology.

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