Graph-Based Medical Image Segmentation in 3D and 4D

在体积数据集中有效检测表示对象边界的全局最优表面， 这在许多医学图像分析应用中是重要的并且仍然具有挑战性。本提案涉及 在3-D和4-D中检测最佳单个和多个相互作用表面的具体问题，包括 圆柱形、封闭表面形状和“复杂”形状。允许掺入以下物质的新方法： 将开发最佳表面检测框架中的基于形状的先验知识。 S 计算的可行性是通过将三维图搜索问题转化为一个 在加权有向图中计算最优闭集的问题。结合全局最优性 在优化过程中使用特定于问题的目标函数将有助于应用 方法，以各种各样的医学图像分割问题。 我们假设基于最优图的三维和四维表面检测的图像分割 搜索将在来自 多种医学成像源，提供理论效率和实际适用性。 我们建议： 1)开发并验证一种用于最佳检测单个和多个相互作用表面的方法 适用于3-D和4-D（包括圆柱和闭合表面）的生物医学图像分割。 2)开发并验证3-D和4-D最佳表面检测方法，以保持复杂的拓扑结构。 3)开发并验证一个3-D和4-D的最佳表面检测方法，该方法结合了形状先验 进入分割过程。 开发的方法将进行测试，与国家的最先进的方法今天使用的比较。的 方法的性能将在足够大小的数据样本中进行统计评估。 公共卫生相关性：体积图像扫描仪（例如，计算机断层扫描，磁共振， 超声）在医学中越来越多地可用，然而空间数据的分析通常被执行 一片一片地看。因此，大量的体积信息不能被完全地 被医生利用。诸如这里提出的图像分析方法允许评估图像数据 客观地以定量的方式，有望大大影响基于图像的临床护理。

Efficient detection of globally optimal surfaces representing object boundaries in volumetric datasets is important and remains challenging in many medical image analysis applications. This proposal deals with a specific problem of detecting optimal single and multiple interacting surfaces in 3-D and 4-D,including cylindrical shapes, closed-surface shapes, and "complex" shapes. Novel methods allowing incorporation of shape-based a priori knowledge in the optimal surface detection framework will be developed. s The computational feasibility is accomplished by transforming the 3-D graph-searching problem to a problem of computing an optimal closed set in a weighted directed graph. Combining the global optimality with problem-specific objective functions used in the optimization process will facilitate application of the methods to a wide variety of medical image segmentation problems. We hypothesize that image segmentation based on 3-D and 4-D surface detection utilizing optimal graph searching will provide accurate and robust segmentation performance in volumetric image data from a variety of medical imaging sources, offering theoretical efficiency andpractical applicability. We propose to: 1) Develop and validate a method for optimal detection of single and multiple interacting surfaces applicable to biomedical image segmentation in 3-D and 4-D (including cylindrical and closed surfaces). 2) Develop and validate a 3-D and 4-D optimal surface detection method that preserve complex topologies. 3) Develop and validate a 3-D and 4-D optimal surface detection method that incorporates shape priors into the segmentation process. The developed methods will be tested in comparison with state-of-the-art methods utilized today. The methods' performance will be statistically assessed in data samples of sufficient sizes. Public Health relevance: Volumetric image scanners (e.g., computed tomography, magnetic resonance, ultrasound) are increasingly available in medicine, yet the analysis of spatial data is typically performed visually on a slice-by-slice basis. The large amount of volumetric information therefore cannot be fully utilized by the physicians. Image analysis methods such as proposed here allow evaluating the image data objectively in a quantitative manner, promising to substantially impact image-based clinical care.