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: 8309340
• 负责人: MILAN SONKA
• 金额: $ 37.04万
• 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 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8309340
• 关键词: 3-Dimensional; Address; Adoption; Attention; Biomedical Research; Clinical; Complex; Computational Science; Data; Development; Devices; Drug Formulations; Environment; Foundations; Four-dimensional; Graph; Image; Image Analysis; Knowledge; Medical Imaging; Medicine; Methodology; Methods; Performance; Phase; Physicians; Process; Publications; Research; Sampling; Seminal; Shapes; Slice; Solutions; Source; Surface; Technology; Testing; Time; Ultrasonography; Weight; Work; abstracting; base; bioimaging; clinical practice; cost; design; flexibility; imaging Segmentation; improved; innovation; novel; peer; process optimization; processing speed; response; user-friendly

Abstract: This is a competitive continuation of our Phase-I project. After successfully fulfilling all of its aims, a novel framework for optimal multi-surface and/or multi-object n-D biomedical image segmentation was developed, validated, and its practical utility demonstrated in clinical and translational image analysis tasks. This Phase-II proposal will develop several important extensions addressing identified limitations of the original framework while maintaining the ability of detecting optimal single and multiple interacting surfaces in n-D, including cylindrical shapes, closed-surface shapes, and shapes of complex topology. Novel methods will be developed for incorporation of shape-based a priori knowledge; substantial improvement of processing speed; and for interactive operator-guided segmentation. We hypothesize that by representing the segmentation problem in an arc-weighted graph (instead of the so-far utilized node-weighted graph), the 3-D and 4-D multi-surface multi-object optimal graph searching will offer significantly increased segmentation accuracy and robustness in volumetric image data from a variety of medical imaging sources, offering flexibility and higher processing speed, leading to real-time interactivity and practical applicability. We propose to: 1) Develop and validate a single- and multiple-surface n-D graph-based optimal segmentation method that uses arc-based graph representation, incorporates a priori shape knowledge using hard and soft constraints, and provides shape guidance while utilizing weighted combinations of edge-, region-, and shape-based costs. 2) Develop an approach for parallel (multi-core, multi-threaded) optimal graph search to significantly increase the processing speed and thus improving the method's applicability to higher-dimensional, multiply interacting, and overall larger problems. 3) Develop and evaluate an efficient real-time approach for interactive use of single- and multiple surface segmentations incorporating expert-user guidance while maintaining highly automated character of 3-D or 4-D segmentation. The developed methods will be evaluated against the Phase-I methods to demonstrate statistically significant performance improvements in a variety of tasks with data samples of sufficient sizes.

摘要： 这是我们第一阶段项目的竞争性延续。在成功实现其所有目标后，开发并验证了一种用于优化多表面和/或多对象n-D生物医学图像分割的新框架，并在临床和平移图像分析任务中证明了其实际效用。这个第二阶段的建议将开发几个重要的扩展解决原来的框架确定的局限性，同时保持检测最佳的单一和多个相互作用的能力 n-D中的曲面，包括圆柱形状、闭合曲面形状和复杂拓扑形状。 将开发新的方法，将形状为基础的先验知识，大大提高处理速度，并为交互式操作员引导分割。 我们假设通过在弧加权图中表示分割问题（而不是迄今为止使用的节点加权图），3-D和4-D多表面多对象最佳图搜索将在来自各种医学成像源的体积图像数据中提供显著增加的分割精度和鲁棒性，提供灵活性和更高的处理速度，从而导致实时交互性和实用性。 我们建议： 1)开发并验证基于单表面和多表面n-D图形的最佳分割方法，该方法使用基于弧的图形表示，使用硬约束和软约束结合先验形状知识，并在利用基于边缘、区域和形状的成本的加权组合的同时提供形状指导。 2)开发一种并行（多核、多线程）最佳图搜索方法，以显着提高处理速度，从而提高该方法对更高维度、多重交互和整体更大问题的适用性。 3)开发和评估一种有效的实时方法，用于交互式使用单个和多个表面分割，并结合专家用户指导，同时保持3-D或4-D分割的高度自动化特性。 将对开发的方法进行评估，第一阶段的方法，以证明统计上显着的性能改进，在各种任务与足够大小的数据样本。