Deformation-Based Computational Morphometry for Cell Biology Applications

用于细胞生物学应用的基于变形的计算形态测量

• 批准号: 7945349
• 负责人: Gustavo Kunde Rohde
• 金额: $ 18.5万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7945349
• 关键词: Address; Affect; Age; Algorithms; Area; Arts; Attention; Biological; Biological Phenomena; Biological Process; Biology; Brain; Cations; Cell Nucleus; Cells; Cellular biology; Cleaved cell; Collection; Complex; Computational algorithm; Computer software; Computing Methodologies; Confocal Microscopy; Data; Data Analyses; Descriptor; Drug Formulations; Fluorescence; Foundations; Freedom; Gene Expression; Goals; Hand; Hela Cells; Image; Imaging problem; Informatics; Joints; Lead; Learning; Maps; Measurement; Measures; Medicine; Methodology; Methods; Metric; Microscopic; Microscopy; Organ; Organelles; Pathology; Pharmaceutical Preparations; Phenotype; Population; Process; Property; Research; Research Methodology; Scientist; Shapes; Simulate; Solutions; Specific qualifier value; Statistical Methods; Structure; Surface; System; Technology; Testing; Variant; Work; base; cell motility; computational morphometry; design; high throughput screening; innovation; insight; interest; mathematical algorithm; morphometry; programs; research study; statistics; tool; vector; web site

DESCRIPTION (provided by applicant): Among the many problems arising from cell biology and medicine, shape phenotype mapping (the detailed measurement of shape related quantities from cells and subcellular organelles) from microscopy images has received particular attention in the past due to its relevance to numerous biological processes. We aim to make a fundamental contribution to cellular and subcellular phenotype mapping by developing methods for recover the underlying articulation parameters (deformation modes) of distribution of forms with particular relevance to cell biology. More specifically, we will couple a metric space formulation for biological forms with modern nonlinear manifold learning algorithms for characterizing (determination of mean shape, most likely modes of variation, statistical tests, etc.) shape distributions directly and automatically from image data. A particular innovative aspect of our work will be in allowing for using existing data to guide in the design of statistical tests to differentiate populations of cells (or subcellular organelles). These should allow for more sensitive statistical tests as compared with traditional approaches which are based on testing pre-conceived hypothesis. The methods we propose will be compared to already existing works in an effort to produce a standard, well-accepted technology. The successful completion of this project will have significant impact in areas such as pathology, high-throughput screening, cell motility studies, etc. by enabling one to perform many intricate tasks using a standard methodology. PUBLIC HEALTH RELEVANCE: This research is aimed at supporting high-throughput shape phenotype mapping automatically from microscopic images through the introduction of a rigorous mathematical foundation, accompanied by suitable computational algorithms. More specifically, we will develop methodology for performing high-level shape measurements (determination of mean shape, most likely modes of variation, statistical tests, etc.) based on the spatial transformations that relate two or more forms as depicted in microscopy images. The methods we propose will be compared to already existing works in an effort to produce a standard, well-accepted technology. The successful completion of this project will have significant impact in areas such as pathology, high-throughput screening, cell motility studies, etc. by enabling one to perform many intricate tasks using a standard methodology.

描述（由申请人提供）：在细胞生物学和医学产生的许多问题中，来自显微镜图像的形状表型映射（来自细胞和亚细胞器的形状相关量的详细测量）由于其与许多生物过程的相关性而在过去受到特别关注。我们的目标是通过开发恢复与细胞生物学特别相关的形式分布的基本关节参数（变形模式）的方法，对细胞和亚细胞表型映射做出根本性的贡献。更具体地说，我们将把生物形式的度量空间公式与现代非线性流形学习算法相结合，用于表征（确定平均形状，最可能的变化模式，统计测试等）。形状分布直接和自动从图像数据。我们工作的一个特别创新的方面是允许使用现有数据来指导统计测试的设计，以区分细胞（或亚细胞器）的群体。与基于检验预先设想的假设的传统方法相比，这些方法应允许进行更敏感的统计检验。我们提出的方法将与现有的工作进行比较，以产生一个标准的，广泛接受的技术。该项目的成功完成将对病理学、高通量筛选、细胞运动研究等领域产生重大影响，使人们能够使用标准方法执行许多复杂的任务。 公共卫生关系：本研究的目的是通过引入严格的数学基础，伴随着适当的计算算法，支持高通量的形状表型映射自动从显微图像。更具体地说，我们将开发用于执行高级形状测量的方法（确定平均形状，最可能的变化模式，统计检验等）。基于将显微镜图像中描绘的两种或多种形式相关的空间变换。我们提出的方法将与现有的工作进行比较，以产生一个标准的，广泛接受的技术。该项目的成功完成将对病理学、高通量筛选、细胞运动研究等领域产生重大影响，使人们能够使用标准方法执行许多复杂的任务。