Surgical assessment of liver cancer patients requires identification of the vessel trees from medical images. Specifically, the venous trees - the portal (perfusing) and the hepatic (draining) trees are important for understanding the liver anatomy and disease state, and perform surgery planning. This research aims to improve the 3D segmentation, skeletonization, and subsequent analysis of vessel trees, by creating an automatic pipeline based on deep learning and image processing techniques. The first part of this work explores the impact of differentiable skeletonization methods such as ClDice and morphological skeletonization loss, on the overall liver vessel segmentation performance. To this aim, it studies how to improve vessel tree connectivity. The second part of this study converts a single class vessel segmentation into multi-class ones, separating the two venous trees. It builds on the previous two-class vessel segmentation model, which vessel tree outputs might be entangled, and on connected components and skeleton analyses of the trees. After providing sub-labeling of the specific anatomical branches of each venous tree, these algorithms also enable a morphometric analysis of the vessel trees by extracting various geometrical markers. In conclusion, we propose a method that successfully improves current skeletonization methods, for extensive vascular trees that contain vessels of different calibers. The separation algorithm creates a clean multi-class segmentation of the vessels, validated by surgeons to provide low error. A new, publicly shared high-quality liver vessel dataset of 77 cases is thus created. Finally a method to annotate vessel trees according to anatomy is provided, enabling a unique liver vessel morphometry analysis.

翻译：肝癌患者的手术评估需要从医学影像中识别血管树。具体而言，静脉树——门静脉（灌注）树和肝静脉（引流）树——对于理解肝脏解剖结构、疾病状态以及进行手术规划至关重要。本研究旨在通过构建一个基于深度学习和图像处理技术的自动化流程，改进血管树的三维分割、骨架化及后续分析。本工作的第一部分探讨了可微骨架化方法（如ClDice）和形态学骨架化损失对整体肝脏血管分割性能的影响，重点研究如何提升血管树的连通性。第二部分将单类别血管分割转换为多类别分割，以区分两条静脉树。该部分基于先前的双类别血管分割模型（其血管树输出可能存在纠缠），并结合血管树的连通分量分析与骨架分析。在完成对各静脉树特定解剖分支的子标注后，这些算法还能通过提取多种几何标记实现血管树的形态计量学分析。综上所述，我们提出了一种方法，成功改进了当前针对包含不同管径血管的复杂血管树的骨架化方法。所提出的分离算法生成了清晰的多类别血管分割结果，经外科医生验证具有较低误差。本研究由此创建了一个包含77例病例、公开共享的高质量肝脏血管数据集。最后，提供了一种根据解剖结构标注血管树的方法，实现了独特的肝脏血管形态计量分析。