标题： 基于图注意力引导扩散的连续完整肝脏血管分割 显示英文标题

标题： Continuous and complete liver vessel segmentation with graph-attention guided diffusion

摘要： 提高连通性和完整性是肝脏血管分割中最具有挑战性的方面，尤其是对于小血管而言。 这些挑战需要同时学习连续的血管几何形状并专注于小血管检测。 然而，当前的方法并未明确解决这两个方面，在受不一致标注约束时无法很好地泛化。 在这里，我们利用扩散模型的泛化能力，并在基于扩散的分割模型中显式地整合了连通性和完整性。 具体来说，我们使用了一个图注意力模块，该模块添加了关于血管几何形状的知识。 此外，我们在多尺度上执行图注意力，从而关注小的肝脏血管。 我们的方法在两个公开数据集（3D-ircadb-01 和 LiVS）上优于五种最先进的医学分割方法。 显示英文摘要

摘要： Improving connectivity and completeness are the most challenging aspects of liver vessel segmentation, especially for small vessels. These challenges require both learning the continuous vessel geometry and focusing on small vessel detection. However, current methods do not explicitly address these two aspects and cannot generalize well when constrained by inconsistent annotations. Here, we take advantage of the generalization of the diffusion model and explicitly integrate connectivity and completeness in our diffusion-based segmentation model. Specifically, we use a graph-attention module that adds knowledge about vessel geometry. Additionally, we perform the graph-attention at multiple-scales, thus focusing on small liver vessels. Our method outperforms five state-of-the-art medical segmentation methods on two public datasets: 3D-ircadb-01 and LiVS.