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

对体数据集中表示对象边界的全局最优表面的有效检测是 在许多医学图像分析应用中，这一点非常重要，仍然具有挑战性。这项建议涉及的是 在3-D和4-D中检测最佳单个和多个交互曲面的具体问题，包括 圆柱形、闭合面形和“复杂”形。允许合并的新方法 将在最优表面检测框架中开发基于形状的先验知识。 S 计算的可行性是通过将三维图形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 方法针对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优图 搜索将提供准确且稳健的分割性能 多种医学影像来源，提供理论效率和实用适用性。 我们建议： 1)开发并验证了单个和多个相互作用表面的最优检测方法 适用于三维和四维(包括柱面和闭合曲面)的生物医学图像分割。 2)开发并验证了一种保持复杂拓扑结构的三维和四维最优表面检测方法。 3)提出并验证了一种结合形状先验的3-D和4-D最佳表面检测方法 进入分割过程。 开发的方法将与目前使用的最先进的方法进行比较测试。这个 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)在医学上日益可用，但通常执行空间数据分析 在可视的基础上逐个切片。因此，大量的体积信息不能完全 被内科医生利用。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，有望对基于图像的临床护理产生重大影响。

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.