SGER: Scalable Shape Analysis in Non-Euclidean Spaces with Provable Guarantees

SGER：具有可证明保证的非欧几里得空间中的可扩展形状分析

• 批准号: 0841185
• 负责人: Suresh Venkatasubramanian
• 金额: --
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0841185&HistoricalAwards=false
• 关键词: SGER; Scalable; Shape; Analysis; Non

ABSTRACTAdvances in medical imaging technology have allowed clinicians to generate vast amounts of shape data describing various parts of the human body. In doing so, the goal is to obtain precise statistical notions of what a "normal" morphology looks like, and what the different types of "abnormal" morphology look like, so that clinicians can target treatment to patients based on these classifications.Developing tools for the statistical analysis of morphology (i.e. shape) has the potential to transform medical imaging processes in a fundamental way, allowing us to understand at a large scale the normal and abnormal variations in human organs and provide a statistical "map" of the human body. In this research, the PI develops new tools for the statistical analysis of shape by combining the mathematics of non-Euclidean shape spaces with techniques for approximating and efficiently manipulating such spaces.

摘要医学成像技术的进步使临床医生能够生成大量描述人体各个部位的形状数据。 这样做的目的是获得关于“正常”形态的精确统计概念，以及不同类型的“异常”形态的精确统计概念，以便临床医生可以根据这些分类对患者进行针对性治疗。（即形状）具有以基本方式改变医学成像过程的潜力，使我们能够大规模地了解人体器官的正常和异常变化，并提供人体的统计“地图”。在这项研究中，PI通过将非欧几里德形状空间的数学与近似和有效操纵这些空间的技术相结合，开发了用于形状统计分析的新工具。