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代表物体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。该方案处理了一个特定的问题，即在3-D和4-D中检测最优的单个和多个相互作用的表面，包括圆柱形、封闭表面形状和“复杂”形状。新的方法允许结合形状为基础的先验知识，在最优的表面检测框架将被开发。将三维图搜索问题转化为加权有向图的最优闭集计算问题，实现了计算可行性。将优化过程中的全局最优性与问题特定目标函数相结合，将促进该方法在各种医学图像分割问题中的应用。我们假设基于最优图搜索的三维和四维表面检测的图像分割将对来自各种医学成像源的体积图像数据提供准确和鲁棒的分割性能，具有理论效率和实际适用性。我们建议：1)开发并验证一种适用于3-D和4-D（包括圆柱形和封闭表面）生物医学图像分割的单和多个相互作用表面的最佳检测方法。2)开发并验证保留复杂拓扑结构的3-D和4-D最佳表面检测方法。3)开发并验证了将形状先验纳入分割过程的3- d和4-D最优表面检测方法。开发的方法将与目前使用的最先进的方法进行比较。这些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（如计算机断层扫描、磁共振、超声波）越来越多地用于医学，但空间数据的分析通常是在逐片的基础上进行的。因此，大量的体积信息不能被医生充分利用。这里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，有望实质性地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.