Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Department of Surgery, Chonbuk National University Medical School, Jeonju, Republic of Korea; Research Institute of Clinical Medicine of Chonbuk National University, Jeonju, Republic of Korea; Biomedical Research Institute of Chonbuk University Hospital, Jeonju, Republic of Korea.
Comput Methods Programs Biomed. 2018 May;158:11-19. doi: 10.1016/j.cmpb.2018.01.024. Epub 2018 Jan 31.
Manual tracing of the right and left liver lobes from computed tomography (CT) images for graft volumetry in preoperative surgery planning of living donor liver transplantation (LDLT) is common at most medical centers. This study aims to develop an automatic system with advanced image processing algorithms and user-friendly interfaces for liver graft volumetry and evaluate its accuracy and efficiency in comparison with a manual tracing method.
The proposed system provides a sequential procedure consisting of (1) liver segmentation, (2) blood vessel segmentation, and (3) virtual liver resection for liver graft volumetry. Automatic segmentation algorithms using histogram analysis, hybrid level-set methods, and a customized region growing method were developed. User-friendly interfaces such as sequential and hierarchical user menus, context-sensitive on-screen hotkey menus, and real-time sound and visual feedback were implemented. Blood vessels were excluded from the liver for accurate liver graft volumetry. A large sphere-based interactive method was developed for dividing the liver into left and right lobes with a customized cutting plane. The proposed system was evaluated using 50 CT datasets in terms of graft weight estimation accuracy and task completion time through comparison to the manual tracing method. The accuracy of liver graft weight estimation was assessed by absolute difference (AD) and percentage of AD (%AD) between preoperatively estimated graft weight and intraoperatively measured graft weight. Intra- and inter-observer agreements of liver graft weight estimation were assessed by intraclass correlation coefficients (ICCs) using ten cases randomly selected.
The proposed system showed significantly higher accuracy and efficiency in liver graft weight estimation (AD = 21.0 ± 18.4 g; %AD = 3.1% ± 2.8%; percentage of %AD > 10% = none; task completion time = 7.3 ± 1.4 min) than the manual tracing method (AD = 70.5 ± 52.1 g; %AD = 10.2% ± 7.5%; percentage of %AD > 10% = 46%; task completion time = 37.9 ± 7.0 min). The proposed system showed slightly higher intra- and inter-observer agreements (ICC = 0.996 to 0.998) than the manual tracing method (ICC = 0.979 to 0.999).
The proposed system was proved accurate and efficient in liver graft volumetry for preoperative planning of LDLT.
在活体肝移植（LDLT）术前手术计划中，从计算机断层扫描（CT）图像手动追踪右肝和左肝叶以进行供体肝体积测量，这在大多数医疗中心是常见的。本研究旨在开发一种具有先进图像处理算法和用户友好界面的自动系统，用于供体肝体积测量，并将其准确性和效率与手动追踪方法进行比较。
所提出的系统提供了一个连续的过程，包括（1）肝脏分割，（2）血管分割，和（3）虚拟肝切除以进行供体肝体积测量。使用直方图分析、混合水平集方法和定制的区域生长方法开发了自动分割算法。实现了用户友好的界面，例如顺序和分层用户菜单、上下文相关的屏幕热键菜单以及实时声音和视觉反馈。为了准确测量供体肝体积，将血管从肝脏中排除。开发了一种大型基于球体的交互式方法，用于使用定制的切割平面将肝脏分为左叶和右叶。通过与手动追踪方法进行比较，使用 50 个 CT 数据集评估了该系统在移植肝重量估计准确性和任务完成时间方面的性能。通过术前估计的移植肝重量与术中测量的移植肝重量之间的绝对差值（AD）和 AD 的百分比（%AD）评估移植肝重量估计的准确性。使用随机选择的十个病例评估肝移植重量估计的观察者内和观察者间一致性，采用组内相关系数（ICC）。
与手动追踪方法相比，该系统在供体肝重量估计方面具有更高的准确性和效率（AD=21.0±18.4 g；%AD=3.1%±2.8%；%AD>10%=无；任务完成时间=7.3±1.4 min）。该系统显示出稍高的观察者内和观察者间一致性（ICC=0.996 至 0.998），而手动追踪方法的观察者内和观察者间一致性（ICC=0.979 至 0.999）。
所提出的系统在 LDLT 术前计划中证明了其在供体肝体积测量方面的准确性和高效性。
