Purpose Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. Methods The proposed method consists of two main steps:(i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map.
Results The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance
(MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE,RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and33.14 mm, respectively.Conclusions The proposed method is fully automatic without any user interaction. Quantitative results reveal that the
proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the
time-consuming and nonreproducible manual segmentation method.
目的从腹部计算机断层扫描（CT）图像中分割肝脏是一些计算机辅助临床干预的重要步骤，例如活体肝移植的手术规划，放疗和体积测量。在这项工作中，我们开发了一种图形切割细化的深度学习算法，可以在CT扫描中自动分割肝脏。方法所提出的方法由两个主要步骤组成：（i）使用三维卷积神经网络同时进行肝脏检测和概率分割; （ii）使用图形切割和先前学习的概率图精确地完成初始分割。
结果所提出的方法在来自两个公共数据库MICCAI-Sliver07和3Dircadb1的四十个CT体积上进行了验证。对于MICCAI-Sliver07测试数据集，计算的体积重叠误差（VOE），相对体积差（RVD），平均对称表面距离（ASD），均方根对称表面距离（RMSD）和最大对称表面积距离
（MSD）分别为5.9,2.7％，0.91,1.88和18.94mm。对于3Dircadb1数据集，计算出的VOE，RVD，ASD，RMSD和MSD的平均比值分别为9.36,0.97％，1.89,4.15和33.14mm。结论本文提出的方法是全自动的，无需任何用户交互。定量结果揭示了
所提出的方法对于临床设置中的肝体积估计是高效且准确的。自动和手动参考之间的高度相关性表明，所提出的方法可以很好地代替耗时且不可重现的手动分割方法。