Salman Al-Shaikhli Saif Dawood, Yang Michael Ying, Rosenhahn Bodo
Biomed Tech (Berl). 2016 Aug 1;61(4):401-12. doi: 10.1515/bmt-2015-0017.
Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.
自动三维肝脏分割是肝脏疾病诊断和手术规划中的一个基本步骤。本文提出了一种用于临床三维计算机断层扫描（CT）图像中三维肝脏分割的全新全自动算法。基于图像特征，我们使用主动形状模型（ASM）提出了一种新的马氏距离代价函数。我们将我们的方法称为MD - ASM。与标准主动形状模型（ST - ASM）不同，该方法引入了一种新的特征约束马氏距离代价函数，以测量迭代步骤中生成的形状与平均形状模型之间的距离。所提出的马氏距离函数是从肝脏分割挑战的公共数据库（MICCAI - SLiver07）中学习得到的。作为细化步骤，我们提出使用三维图割分割。利用学习到的马氏距离的纹理特征自动选择前景和背景标签。在定量方面，使用两个临床三维CT扫描数据库（MICCAI - SLiver07和MIDAS）对所提出的方法进行评估。MICCAI - SLiver07数据库的评估由挑战组织者使用五种不同的度量分数获得。实验结果表明，与现有方法相比，所提出的方法通过实现准确的肝脏分割证明了其可用性。
