College of Computer Science, Chongqing University, Chongqing 400044, People's Republic of China.
Phys Med Biol. 2018 Jan 16;63(2):025024. doi: 10.1088/1361-6560/aaa360.
Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of [Formula: see text], the best root mean square symmetric surface distance (RMSD) of [Formula: see text] mm, the best maximum symmetric surface distance (MSD) of [Formula: see text] mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of [Formula: see text] mm, the best RMSD of [Formula: see text] mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.
医学图像分割在数字医学研究、治疗计划和治疗中都发挥着重要作用。然而，存在噪声和低对比度使得自动肝脏分割成为一项极具挑战性的任务。在本研究中，我们专注于使用变分方法对计算机断层扫描体积中的肝脏进行半自动、逐片分割。在这种方法中，选择一片并手动确定其连通分量的肝脏区域以初始化后续的自动分割过程。从这个引导切片，我们分别向下到最后一片和向上到第一片执行所提出的方法。通过在水平集框架下结合统计形状先验、全局高斯强度分析和强制局部统计特征，提出了一种分割能量函数。在分割过程中，通过最小化这个函数来估计肝脏形状。使用改进的 Chan-Vese 模型来细化形状以捕捉肝脏的长而窄区域。在两个独立的公共数据库 3D-IRCADb 和 SLIVER07 上验证了所提出的方法。在所测试的所有方法中，我们的方法在 3D-IRCADb 数据集上的体积重叠误差（VOE）为[Formula: see text]，均方根对称面距离（RMSD）为[Formula: see text]mm，最大对称面距离（MSD）为[Formula: see text]mm，在 SLIVER07 数据集上的平均对称面距离（ASD）为[Formula: see text]mm，均方根误差（RMSD）为[Formula: see text]mm，分别达到了最佳效果。定量比较的结果表明，所提出的肝脏分割方法在分割性能方面具有竞争力。
