The accurate modeling of the liver vessel network structure is an important prerequisite for developing a preoperative plan for the liver. Considering that extracting liver blood vessels from patient's abdominal computed tomography(CT) images requires several manual operations, this study proposed an automatic segmentation method of liver vessels based on graph cut, thinning, and vascular combination, which can obtain a complete liver vascular network. First, the CT image was preprocessed by grayscale mapping based on sigmoid function, vessel enhancement based on Hessian filter, and denoising based on anisotropic filter to enhance the grayscale contrast between the vascular and non-vascular parts of the liver. Then, the liver vessels were initially segmented based on the improved three-dimensional graph cut algorithm. Based on the obtained liver vascular structure, the vessel centerline of the liver was then extracted by the proposed thinning algorithm that continuously traversed the foreground voxel points and iteratively deleted the simple points. Finally, the combination of vascular centerline optimization was used to predict and link the vascular centerline fractured portion. The under-segmented liver vessels were complemented based on the complete vascular centerline tree. To verify the proposed hepatic vascular segmentation and complementation algorithm, the open 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb) was applied to test and quantify the results. The results showed that the proposed algorithm can accurately and effectively segment the vascular network structure from abdominal CT images, and the proposed vascular complementation method can restore the true information of under-segmented liver vessels. Graphical abstract A novel hepatic vessel segmentation method from abdominal CT images was proposed, including graph cut algorithm, centerline extraction, and broken vessel completion. First, the graph cut algorithm was used to obtain the initial segmentation result. Then, the centerline of the initial segmentation result was extracted. Finally, the initial segmentation result was optimized through centerline analysis.

中文翻译：

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一种使用血管分割，细化和完成来建模肝血管网络的新方法。

肝血管网络结构的准确建模是制定肝脏术前计划的重要前提。考虑到从患者腹部CT图像中提取肝血管需要几次人工操作，本研究提出了一种基于图割，细化和血管结合的肝血管自动分割方法，可以获得完整的肝血管网络。首先，通过基于S形函数的灰度映射，基于Hessian过滤器的血管增强和基于各向异性过滤器的降噪对CT图像进行预处理，以增强肝脏血管和非血管部分之间的灰度对比度。然后，首先基于改进的三维图切割算法对肝血管进行分割。基于所获得的肝脏血管结构，然后通过提出的细化算法提取肝脏的血管中心线，该细化算法连续遍历前景体素点并迭代地删除简单点。最后，结合使用血管中心线优化来预测和链接血管中心线骨折部位。根据完整的血管中心线树对节段不足的肝血管进行补充。为了验证所提出的肝血管分割和补充算法，将开放式3D图像重建算法数据库比较（3Dircadb）用于测试和量化结果。结果表明，该算法可以从腹部CT图像中准确有效地分割血管网络结构，提出的血管补充方法可以恢复肝段不全的真实信息。提出了一种从腹部CT图像中进行肝血管分割的新方法，包括图割算法，中心线提取和血管破裂完成。首先，使用图割算法获得初始分割结果。然后，提取初始分割结果的中心线。最后，通过中心线分析对初始分割结果进行了优化。使用图割算法获得初始分割结果。然后，提取初始分割结果的中心线。最后，通过中心线分析对初始分割结果进行了优化。使用图割算法获得初始分割结果。然后，提取初始分割结果的中心线。最后，通过中心线分析优化了初始分割结果。