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

基于图的 3D 和 4D 医学图像分割

• 批准号: 7344794
• 负责人: MILAN SONKA
• 金额: $ 33.99万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7344794
• 关键词: 3-Dimensional; Address; Algorithms; Arts; Attention; Automobile Driving; Biomedical Research; Caring; Clinical; Complex; Computational Science; Computers; Data; Data Set; Detection; Devices; Environment; Foundations; Graph; Image; Image Analysis; Imagery; Knowledge; Magnetic Resonance; Medical Imaging; Medicine; Methodology; Methods; NIH Program Announcements; Numbers; Organ; Performance; Physicians; Process; Public Health; Research; Research Personnel; Running; Sampling; Scanning; Shapes; Slice; Solutions; Source; Standards of Weights and Measures; Structure; Surface; Technology; Testing; Three-Dimensional Image; Three-Dimensional Imaging; Time; Today; Ultrasonography; Weight; Work; X-Ray Computed Tomography; base; bioimaging; biomedical Computer science; design; imaging Segmentation; improved; innovation; interest; multidisciplinary; novel; practical application; response; size; theories; tool; user-friendly

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.

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)开发并验证可保留复杂拓扑结构的3D和4D最佳表面检测方法。 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。