Biomedical Engineering, Science and Technology Division, Institute of Clinical Physiology, National Research Council, Campus Ecotekne, Lecce, Italy.
Acad Radiol. 2011 Apr;18(4):461-70. doi: 10.1016/j.acra.2010.11.015. Epub 2011 Jan 8.
The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery.
A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.
The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively.
A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections.
本研究旨在确定一种新的全自动肝脏血管分割算法的最佳参数配置，评估其在三维（3D）肝脏手术规划计算机系统中的应用精度。
采用立体光刻技术，利用源自真实人体 CT 数据集的 3D 模型，制作了包含四级以下分支血管（最小直径 0.2mm）的肝脏血管铸型。通过对增强 CT 血管铸型图像的自动分割与实际模型特征的分析比较，对算法的参数配置进行了实验优化，并对二维单张切片和 3D 血管重建的最大分割精度进行了量化。
最佳算法配置可使直径大于 1mm 的血管检测灵敏度达到 100%，0.5-1mm 之间为 50%，0.2-0.5mm 之间为 14%。自动和手动分割血管段之间的平均面积重叠率为 94.9%，平均差异为 0.06mm2。对应的假阳性和假阴性比值的平均值分别为 7.7%和 2.3%。
提出了一种稳健、准确的从增强 CT 容积图像中提取肝脏血管树的自动分割算法，并在肝脏模型上进行了实验评估，该算法在血管勾画方面具有前所未有的灵敏度。这种自动分割算法有望为肝脏手术规划提供支持，并指导术中切除。
