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: 8120451
• 负责人: MILAN SONKA
• 金额: $ 36.81万
• 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 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8120451
• 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; base; bioimaging; clinical practice; cost; design; flexibility; imaging Segmentation; improved; innovation; novel; peer; process optimization; processing speed; public health relevance; response; user-friendly

DESCRIPTION (provided by applicant): 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 multiplesurface 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. PUBLIC HEALTH RELEVANCE: Project Narrative Three- and four-dimensional (3D + time) analysis of medical image data from MR, CT, ultrasound, or OCT scanners is still performed visually and frequently either non- quantitatively, or only in 2-D slices. Clearly, the 3-D character of the image data provides additional information that may be overlooked by current approaches. The proposed research work is for development of globally optimal image segmentation methods that are practical in 3-D, 4-D and generally n-D medical image data. As such, the study has a promise for facilitating routine clinical analyses of volumetric data from medical image scanners.

描述(由申请者提供)：这是我们第一阶段项目的竞争性延续。在成功地实现了所有目标之后，开发了一种新的多表面和/或多目标n-D生物医学图像分割的新框架，并对其进行了验证，并在临床和平移图像分析任务中展示了其实用价值。这个第二阶段的提案将开发几个重要的扩展，以解决原始框架的已识别限制，同时保持在n-D中检测最佳单个和多个相互作用的表面的能力，包括圆柱形、闭合曲面形状和复杂拓扑形状。将开发新的方法来结合基于形状的先验知识；显著提高处理速度；以及用于交互式操作员指导的分割。我们假设，通过将分割问题表示为圆弧加权图(而不是迄今使用的节点加权图)，三维和四维多表面多对象最优图搜索将显著提高来自各种医学成像源的体图像数据的分割精度和稳健性，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)开发和验证一种基于单面和多面n-D图的优化分割方法，该方法使用基于圆弧的图形表示，使用硬约束和软约束结合先验形状知识，并利用基于边、区域和形状的加权组合来提供形状指导。2)开发了一种并行(多核、多线程)最优图搜索方法，显著提高了处理速度，从而提高了该方法对高维、多交互和全局更大问题的适用性。3)开发和评估一种高效的实时方法，用于交互使用结合了专家-用户指导的单表面和多表面分割，同时保持3-D或4-D分割的高度自动化特征。将对照第一阶段方法对所开发的方法进行评估，以证明在具有足够大小的数据样本的各种任务中具有统计上显著的性能改进。 与公共卫生相关：对来自MR、CT、超声波或OCT扫描仪的医学图像数据进行项目叙述性三维和四维(3D+时间)分析仍然以视觉方式频繁地进行，或者非定量地，或者仅在2-D切片中进行。显然，图像数据的3-D特征提供了可能被当前方法忽略的附加信息。本文的研究工作是为了开发一种全局最优的图像分割方法，该方法适用于三维、四维以及一般的n维医学图像数据。因此，这项研究有望促进对来自医学图像扫描仪的体积数据的常规临床分析。