Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
Department of Interventional Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Comput Biol Med. 2018 Apr 1;95:198-208. doi: 10.1016/j.compbiomed.2018.02.012. Epub 2018 Feb 22.
Liver segmentation from CT images is a fundamental step in trajectory planning for computer-assisted interventional surgery. In this paper, we present a fully automatic procedure using modified graph cuts and feature detection for accurate and fast liver segmentation.
The initial slice and seeds of graph cuts are automatically determined using an intensity-based method with prior position information. A contrast term based on the similarities and differences of local organs across multi-slices is proposed to enhance the weak boundaries of soft organs and to prevent over-segmentation. The term is then integrated into the graph cuts for automatic slice segmentation. Patient-specific intensity and shape constraints of neighboring slices are also used to prevent leakage. Finally, a feature detection method based on vessel anatomical information is proposed to eliminate the adjacent inferior vena cava with similar intensities.
We performed experiments on 20 Sliver07, 20 3Dircadb datasets and local clinical datasets. The average volumetric overlap error, volume difference, symmetric surface distance and volume processing time were 5.3%, -0.6%, 1.0 mm, 17.8 s for the Sliver07 dataset and 8.6%, 0.7%, 1.6 mm, 12.7 s for the 3Dircadb dataset, respectively.
The proposed method can effectively extract the liver from low contrast and complex backgrounds without training samples. It is fully automatic, accurate and fast for liver segmentation in clinical settings.
从 CT 图像中进行肝脏分割是计算机辅助介入手术轨迹规划的基本步骤。本文提出了一种使用改进的图割和特征检测的全自动方法，用于进行准确快速的肝脏分割。
利用基于强度的方法，结合先验位置信息，自动确定图割的初始切片和种子。提出了一种基于多切片局部器官相似性和差异性的对比项，用于增强软组织弱边界，防止过度分割。该术语随后被集成到图割中，以实现自动切片分割。还使用相邻切片的患者特定强度和形状约束来防止泄漏。最后，提出了一种基于血管解剖信息的特征检测方法，用于消除具有相似强度的相邻下腔静脉。
我们在 20 个 Sliver07 、20 个 3Dircadb 数据集和本地临床数据集上进行了实验。Sliver07 数据集的平均体积重叠误差、体积差异、对称表面距离和体积处理时间分别为 5.3%、-0.6%、1.0 mm 和 17.8 s，3Dircadb 数据集的分别为 8.6%、0.7%、1.6 mm 和 12.7 s。
所提出的方法可以在没有训练样本的情况下，从低对比度和复杂背景中有效地提取肝脏。它是完全自动的，在临床环境中进行肝脏分割既准确又快速。
