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.

翻译：血管分割与中心线提取是许多面向血管疾病的计算机辅助诊断工具中两项关键的预处理任务。近年来，基于深度学习的方法已广泛应用于这些任务。然而，经典深度学习方法难以捕捉血管网络的复杂几何形态和特定拓扑结构——而这在大多数应用中至关重要。为克服这些限制，近期提出了clDice损失函数，这是一种聚焦于血管中心线的拓扑损失函数。该损失函数需要借助提出的软骨架算法，分别计算真实分割标签和预测分割结果的骨架结构。但该软骨架算法在三维图像上表现欠佳，导致clDice难以适用于三维图像。本文提出用U-Net替代软骨架算法，直接从分割结果中计算血管骨架。实验表明，我们的方法生成的骨架比软骨架算法更为精确。在此基础上构建级联U-Net结构，通过clDice损失函数进行训练以嵌入拓扑约束，最终模型能够同时预测血管分割结果和中心线，且具有更优的拓扑准确性。