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

描述（由申请人提供）：这是我们第一期项目的竞争性延续。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/或多目标n-D生物医学图像分割的新框架，并在临床和翻译图像分析任务中展示了其实际效用。第二阶段的提案将开发几个重要的扩展，解决原始框架的局限性，同时保持在n-D中检测最佳单个和多个相互作用表面的能力，包括圆柱形，封闭表面形状和复杂拓扑形状。将开发新的方法来整合基于形状的先验知识；处理速度大幅提升；以及交互式操作符引导的分割。我们假设，通过用圆弧加权图来表示分割问题（而不是目前使用的节点加权图），三维和四维多面多目标最优图搜索将显著提高来自各种医学成像源的体积图像数据的分割精度和鲁棒性，提供灵活性和更高的处理速度，从而实现实时交互性和实用性。我们建议：1)开发并验证一种基于单曲面和多曲面n-D图的最优分割方法，该方法使用基于弧的图表示，结合使用硬约束和软约束的先验形状知识，并在利用基于边缘、区域和形状的加权组合成本的同时提供形状指导。2)开发一种并行（多核、多线程）最优图搜索方法，显著提高处理速度，从而提高该方法对高维、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，用于结合专家用户指导的单表面和多表面分割的交互式使用，同时保持3- d或4-D分割的高度自动化特征。开发的方法将根据第一阶段的方法进行评估，以证明在具有足够规模的数据样本的各种任务中统计上显着的性能改进。

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.