Foruzan Amir H, Chen Yen-Wei, Hori Masatoshi, Sato Yoshinobu, Tomiyama Noriyuki
Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran.
Intelligent Image Processing Lab, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan.
Int J Comput Assist Radiol Surg. 2014 Nov;9(6):967-77. doi: 10.1007/s11548-014-1000-5. Epub 2014 Apr 20.
Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to external forces or body position changes during scanning procedures. We developed and tested a population-based model to represent the shape of liver.
Upper abdominal CT-scan input images are represented by a conventional shape model. The shape parameters of individual livers extracted from the CT scans are employed to classify them into different populations. Corresponding to each population, an SSM model is built. The liver surface parameter space is divided into several subspaces which are more compact than the original space. The proposed model was tested using 29 CT-scan liver image data sets. The method was evaluated by model compactness, reconstruction error, generality and specificity measures.
The proposed model is implemented and tested using CT scans that included liver shapes with large shape variations. The method was compared with conventional and recently developed shape modeling methods. The accuracy of the proposed model was nearly twice that achieved with the conventional model. The proposed population-based model was more general compared with the conventional model. The mean reconstruction error of the proposed model was 0.029 mm while that of the conventional model was 0.052 mm.
A population-based model to represent the shape of liver was developed and tested with favorable results. Using this approach, the liver shapes from CT scans were modeled by a more compact, more general, and more accurate model.
统计形状模型（SSMs）可表示特定物体的形态变化。当存在较大形状变化时，形状参数构成的空间可能很大，其中可能包含错误参数。人类肝脏是一个非刚性器官，在扫描过程中会因外力或身体位置变化而发生较大变形。我们开发并测试了一种基于群体的模型来表示肝脏的形状。
上腹部CT扫描输入图像由传统形状模型表示。从CT扫描中提取的个体肝脏的形状参数用于将它们分类到不同群体中。对应于每个群体，构建一个SSM模型。肝脏表面参数空间被划分为几个比原始空间更紧凑的子空间。使用29个CT扫描肝脏图像数据集对所提出的模型进行测试。通过模型紧凑性、重建误差、通用性和特异性度量对该方法进行评估。
所提出的模型通过包含具有较大形状变化的肝脏形状的CT扫描来实现和测试。将该方法与传统的和最近开发的形状建模方法进行比较。所提出模型的准确性几乎是传统模型的两倍。与传统模型相比，所提出的基于群体的模型更具通用性。所提出模型的平均重建误差为0.029毫米，而传统模型的平均重建误差为0.052毫米。
开发并测试了一种基于群体的模型来表示肝脏的形状，结果良好。使用这种方法，通过一个更紧凑、更通用和更准确的模型对CT扫描中的肝脏形状进行建模。
