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

描述（由申请人提供）：在体积数据集中有效检测表示对象边界的全局最优表面是重要的，并且在许多医学图像分析应用中仍然具有挑战性。该建议涉及一个特定的问题，检测最佳的单一和多个相互作用的表面在3-D和4-D，包括圆柱形形状，封闭的表面形状，和“复杂”的形状。新的方法，允许纳入基于形状的先验知识的最佳表面检测框架将被开发。 通过将三维图搜索问题转化为加权有向图中最优闭集的计算问题，实现了计算的可行性。将全局最优性与优化过程中使用的特定于问题的目标函数相结合，将有助于将该方法应用于各种各样的医学图像分割问题。我们假设，基于3-D和4-D表面检测的图像分割，利用最佳图搜索将提供准确和强大的分割性能，从各种医学成像源的体积图像数据，提供理论效率和实用性。我们建议：1）开发并验证一种方法，用于最佳检测单个和多个相互作用的表面，适用于3-D和4-D（包括圆柱形和封闭表面）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)开发并验证一种3-D和4-D最佳表面检测方法，该方法将形状先验纳入分割过程。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。将在足够大小的数据样本中对方法的性能进行统计评估。公共卫生相关性：体积图像扫描仪（例如，计算机断层摄影术、磁共振、超声）在医学中越来越有用，但是空间数据的分析通常是在逐切片的基础上可视地执行的。因此，大量的体积信息不能被医生充分利用。诸如本文提出的图像分析方法允许以定量的方式客观地评估图像数据，有望显著影响基于图像的临床护理。

DESCRIPTION (provided by applicant): 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. 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 and practical 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.