Department of Systems Biology, Harvard Medical School Boston, MA, USA.
Front Neuroinform. 2013 Dec 26;7:35. doi: 10.3389/fninf.2013.00035. eCollection 2013.
In the last decade, level-set methods have been actively developed for applications in image registration, segmentation, tracking, and reconstruction. However, the development of a wide variety of level-set PDEs and their numerical discretization schemes, coupled with hybrid combinations of PDE terms, stopping criteria, and reinitialization strategies, has created a software logistics problem. In the absence of an integrative design, current toolkits support only specific types of level-set implementations which restrict future algorithm development since extensions require significant code duplication and effort. In the new NIH/NLM Insight Toolkit (ITK) v4 architecture, we implemented a level-set software design that is flexible to different numerical (continuous, discrete, and sparse) and grid representations (point, mesh, and image-based). Given that a generic PDE is a summation of different terms, we used a set of linked containers to which level-set terms can be added or deleted at any point in the evolution process. This container-based approach allows the user to explore and customize terms in the level-set equation at compile-time in a flexible manner. The framework is optimized so that repeated computations of common intensity functions (e.g., gradient and Hessians) across multiple terms is eliminated. The framework further enables the evolution of multiple level-sets for multi-object segmentation and processing of large datasets. For doing so, we restrict level-set domains to subsets of the image domain and use multithreading strategies to process groups of subdomains or level-set functions. Users can also select from a variety of reinitialization policies and stopping criteria. Finally, we developed a visualization framework that shows the evolution of a level-set in real-time to help guide algorithm development and parameter optimization. We demonstrate the power of our new framework using confocal microscopy images of cells in a developing zebrafish embryo.
在过去的十年中，水平集方法在图像配准、分割、跟踪和重建等领域得到了积极的发展。然而，各种各样的水平集偏微分方程及其数值离散化方案的发展，再加上偏微分方程项、停止准则和重新初始化策略的混合组合，造成了软件物流方面的问题。在缺乏综合设计的情况下，当前的工具包仅支持特定类型的水平集实现，这限制了未来的算法发展，因为扩展需要大量的代码重复和努力。在新的 NIH/NLM Insight Toolkit (ITK) v4 架构中，我们实现了一种灵活的水平集软件设计，可以适应不同的数值（连续、离散和稀疏）和网格表示（点、网格和基于图像）。由于通用偏微分方程是不同项的总和，我们使用了一组链接容器，其中可以在演化过程中的任何时候添加或删除水平集项。这种基于容器的方法允许用户在编译时以灵活的方式探索和自定义水平集方程中的项。该框架进行了优化，以避免在多个项中重复计算常见的强度函数（例如梯度和 Hessians）。该框架还支持多个水平集的演化，用于多目标分割和处理大型数据集。为此，我们将水平集域限制为图像域的子集，并使用多线程策略来处理子域或水平集函数的组。用户还可以从各种重新初始化策略和停止准则中进行选择。最后，我们开发了一个可视化框架，可以实时显示水平集的演化，以帮助指导算法开发和参数优化。我们使用发育中的斑马鱼胚胎的共聚焦显微镜图像展示了我们新框架的强大功能。
