基于层级血管树的肝脏分段方法

针对经典肝脏功能性分段方法对门静脉血管数据的敏感性，结合Couinaud肝脏分段理论和门静脉分布特征，提出了基于层级血管树的肝脏分段方法：首先对腹腔CT数据进行肝脏分割、血管提取和骨架化；接着统计分析血管树分支半径，确定二级子树集合，按照供血区域对二级子树进行聚类完成对二级子树的归类划分；进而采用最短距离归类算法划分肝脏，得到各个肝段；最后运用三维可视化方法展现肝脏内部的解剖结构，并进行肝段诠析，提取临床感兴趣信息。实验结果表明该方法对分支较多、结构较复杂的血管树可以取得较好的分级效果，考虑了大部分二级分支的供血作用，分割得到的肝段分布和属性信息也符合Couinaud肝段分割理论。

Liver segmentation method based on hierarchical vascular tree

For the sensitivity of the portal vein data to classical liver functional segmentation method, a liver segment method based on hierarchical vascular tree combining with the Couinaud theory and portal vein distribution characteristics is proposed. Firstly, liver and vessels are extracted from the abdominal CT image by image segmentation and skeletonization methods. Secondly, secondary subtree set was determined through statistical analysis on average radius of vascular branches, so as to divide the secondary subtree set into several different classes by k-means++ clustering algorithm according to their own blood-supply area. Thirdly, a nearest neighbor segment approximation algorithm was used to segment the liver into parts. Finally, the internal anatomical structure of liver and its vascular system was demonstrated using three-dimensional visualization technology, and then making annotations on liver segments to extract clinical interest information. Experimental result shows that the method can obtain good results when vascular tree contains plenty branches and complex structure. Furthermore, for considering the impact of major secondary branches, the final liver segment distribution and attribute results are in line with the Couinaud liver segment theory.