Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.
翻译:管状结构、类似网络结构(如船只、神经元或道路)的准确分解与许多研究领域相关。对于这些结构,地形学是其最重要的特征;特别是保护连接性:在血管网络中,缺少连接的容器完全改变血液流动动态。我们引入了一种新型的类似措施,称为中线Dice(shortcldice),该措施是在分解面具及其(形态学)骨骼的交叉点上计算的。我们理论上证明,Cldice保证了表层保持到二维和三维分解的同质等同状态。我们提出,在培训任意神经分解网络时,采用计算效率高、可区分的损失功能(软-cldice)。我们将软链-cldice损失以5个公共数据集作为基准,包括船只、道路和神经元(2D和3D)。软链培训导致分解,以更准确的连通性信息、更高图表相似性和更好的体积分数。