Deformation-Based Computational Morphometry for Cell Biology Applications

描述(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(从细胞和亚细胞细胞器中对形状相关数量的详细测量)在过去受到了特别的关注，因为它与许多生物过程有关。我们的目标是通过开发恢复与细胞生物学特别相关的形态分布的潜在关节参数(变形模式)的方法，为细胞和亚细胞表型图谱做出基础性贡献。更具体地说，我们将把生物形式的度量空间公式与用于表征(确定平均形状、最可能的变化模式、统计测试等)的现代非线性流形学习算法结合起来。形状分布直接从图像数据自动生成。我们工作的一个特别的创新方面将是允许使用现有的数据来指导设计统计测试以区分细胞(或亚细胞细胞器)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，以努力产生一种标准的、被广泛接受的技术。该项目的成功完成将使人们能够使用标准方法执行许多复杂的任务，从而在病理学、高通量筛选、细胞运动性研究等领域产生重大影响。公共卫生相关性：这项研究的目的是通过引入严格的数学基础，并辅之以合适的计算算法，支持从微观图像自动绘制高通量形状表型图。更具体地说，我们将开发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、统计测试等)。基于与显微镜图像中描绘的两个或多个形状相关的空间变换。我们提出的方法将与现有的工作进行比较，以努力产生一种标准的、被广泛接受的技术。该项目的成功完成将使人们能够使用标准方法执行许多复杂的任务，从而在病理学、高通量筛选、细胞运动性研究等领域产生重大影响。

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.