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: 9110984
• 负责人: MILAN SONKA
• 金额: $ 41.29万
• 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 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9110984
• 关键词: 3-Dimensional; Address; Adopted; Affect; Age related macular degeneration; Algorithms; Appearance; Area; Attention; Biomedical Research; Breathing; Caring; Clinical; Complex; Computational Science; Cyst; Data; Devices; Diagnostic; Disease; Environment; Failure; Foundations; Graph; Health; Healthcare; Hour; Image; Image Analysis; Intuition; Machine Learning; Manuals; Medical; Medical Imaging; Medicine; Methodology; Methods; Modification; Nodule; Organ; Outcome; Pathology; Performance; Phase; Positioning Attribute; Process; Publications; Pulmonary Emphysema; Radiation Oncology; Research; Research Project Grants; Retinal; Slice; Speed; Structure; Surface; Techniques; Technology; Time; Translational Research; Update; attenuation; base; bioimaging; clinical application; clinical care; clinical practice; clinically relevant; cost; design; experience; flexibility; imaging Segmentation; innovation; lung imaging; novel; personalized care; quantitative imaging; response; treatment planning; tumor; user-friendly

DESCRIPTION (provided by applicant): This is a competitive continuation of our Phase-II project. After successfully fulfilling all of its aims, our framework for optimal multi-surface andor multi-object n-D biomedical image segmentation was further extended, validated, and its practical utility demonstrated in clinical and translational image analysis tasks. This Phase-III proposal will develop several important extensions addressing identified limitations of the current framework and specifically focusing on applicability of the methodology to translational and routine healthcare tasks. Novel methods will be developed for simultaneous segmentation of mutually interacting regions and surfaces, automated design of cost functions from segmentation examples, and overcoming failures of automated techniques in routine diagnostic quality images by allowing limited and highly efficient expert input to guide the image segmentation processes. We hypothesize that advanced graph-based image segmentation algorithms merging machine- learning-derived segmentation parameters and image-specific expert guidance will significantly increase quantitative analysis performance in routinely acquired complex diagnostic-quality medical images across diverse application areas. We propose to: 1) Develop 3D, 4D, and generally n-D approaches for simultaneous segmentation of mutually interacting regions (objects) and surfaces. 2) Develop methods for data-driven automated design of cost functions used for surface-based, region-based, and surface-and-region-based graph search image segmentation. 3) Develop "Just-Enough-Interaction" (JEI) approaches for efficient "real-time" medical image segmentation, thus achieving robust clinical applicability of quantitative medical image analysis. 4) Assess performance of all developed methods in translational research settings; determine performance in quantitative medical image analysis and radiation oncology treatment planning workflow. As a result, our project will enable routine quantification and therefore personalized care.

描述（由申请人提供）：这是我们的第二阶段项目的竞争延续。在成功实现了所有目标后，我们的多表面和/或多目标n-D生物医学图像分割的最佳框架得到了进一步的扩展，验证，并在临床和平移图像分析任务中证明了其实际效用。该第三阶段提案将开发几个重要的扩展，以解决当前框架的已识别限制，并特别关注该方法对转化和常规医疗保健任务的适用性。将开发新的方法，用于同时分割相互作用的区域和表面，从分割实例中自动设计成本函数，并通过允许有限和高效的专家输入来指导图像分割过程，从而克服常规诊断质量图像中自动化技术的失败。 我们假设，融合机器学习衍生的分割参数和图像特定专家指导的高级基于图形的图像分割算法将显著提高在不同应用领域中常规采集的复杂诊断质量医学图像的定量分析性能。我们建议：1）开发3D，4D和一般的n-D方法，用于同时分割相互作用的区域（对象）和表面。2)开发用于基于表面、基于区域以及基于表面和区域的图搜索图像分割的成本函数的数据驱动自动设计方法。3)开发“足够交互”（JEI）方法，用于有效的“实时”医学图像分割，从而实现定量医学图像分析的强大临床应用。4)评估转化研究环境中所有开发方法的性能;确定定量医学图像分析和放射肿瘤治疗计划工作流程的性能。因此，我们的项目将实现常规量化，从而实现个性化护理。