Sun Center of Excellence for Visual Genomics, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1, Canada.
BMC Med Imaging. 2010 Feb 8;10:5. doi: 10.1186/1471-2342-10-5.
Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limited views of them that medical images can offer, it is still an exceptionally difficult task to quickly and accurately create 3D generic models (a model is a 3D graphical representation of a biological structure) based on medical image stacks (a stack is an ordered collection of 2D images). We show that the creation of a generic model that captures spatial information exploitable in statistical analyses is facilitated by coupling our generalized segmentation method to existing automatic image registration algorithms.
The method of creating generic 3D models consists of the following processing steps: (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) interactively extracting sub-volume by cutting each model to generate the sub-model of interest; (iv) creating image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models.
After several registration procedures are applied to the image stacks, we can create averaged image stacks with sharp boundaries. The averaged 3D model created from those image stacks is very close to the average representation of the population. The image registration time varies depending on the image size and the desired accuracy of the registration. Both volumetric data and surface model for the generic 3D model are created at the final step.
Our method is very flexible and easy to use such that anyone can use image stacks to create models and retrieve a sub-region from it at their ease. Java-based implementation allows our method to be used on various visualization systems including personal computers, workstations, computers equipped with stereo displays, and even virtual reality rooms such as the CAVE Automated Virtual Environment. The technique allows biologists to build generic 3D models of their interest quickly and accurately.
使用 3D 通用模型对生物结构变化趋势进行统计分析是形态计量学研究的重要工具。因此，构建适用于多种人群的 3D 通用模型的需求很高。然而，由于生物结构的复杂性以及医学图像所能提供的有限视角，根据医学图像堆栈（图像堆栈是二维图像的有序集合）快速准确地创建 3D 通用模型（模型是生物结构的三维图形表示）仍然是一项非常困难的任务。我们展示了通过将我们的广义分割方法与现有的自动图像配准算法相结合，可以促进创建可用于统计分析的捕获空间信息的通用模型。
创建通用 3D 模型的方法包括以下处理步骤：（i）扫描受试者以获取图像堆栈；（ii）从堆栈中创建个体 3D 模型；（iii）通过切割每个模型来交互地提取子体积，以生成感兴趣的子模型；（iv）创建仅包含子模型信息的图像堆栈；（v）迭代注册相应的新 2D 图像堆栈；（vi）根据强度平均新创建的子模型，以从所有个体子模型中生成通用模型。
在对图像堆栈应用了几个配准过程之后，我们可以创建具有清晰边界的平均图像堆栈。从这些图像堆栈创建的平均 3D 模型非常接近群体的平均表示。图像注册时间取决于图像大小和所需的注册精度。通用 3D 模型的体数据和表面模型都在最后一步创建。
我们的方法非常灵活且易于使用，任何人都可以使用图像堆栈轻松地创建模型并从中检索子区域。基于 Java 的实现允许我们的方法在各种可视化系统上使用，包括个人计算机、工作站、配备立体显示器的计算机，甚至虚拟现实室，如 CAVE 自动化虚拟环境。该技术允许生物学家快速准确地构建他们感兴趣的通用 3D 模型。
