AUTOMATED SEGMENTATION OF FLUORESCENCE MICROSCOPY DATA SETS

荧光显微镜数据集的自动分割

• 批准号: 7513584
• 负责人: JELENA KOVACEVIC
• 金额: $ 6.93万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7513584
• 关键词: Algorithms; Arts; Biological; Biological Preservation; Biological Sciences; Biology; Cell physiology; Class; Collection; Communities; Complex; Computer software; Data; Data Set; Development; Family; Feedback; Figs - dietary; Fluorescence Microscopy; Fluorescent Probes; Goals; Golgi Apparatus; Green Fluorescent Proteins; Hand; Hour; Human; Image; Manuals; Methods; Microscope; Microscopy; Molecular; Motivation; Movement; Nature; Numbers; Process; Proteins; Range; Research; Services; System; Texture; Time; Tissues; Work; base; day; density; design; fluorescence microscope; image processing; imaging Segmentation; interest; success; user-friendly

DESCRIPTION (provided by applicant): In recent years, the focus in biological science has shifted to understanding complex systems at the cellular and molecular levels, a task greatly facilitated by fluorescence microscopy. Its success is due in part to the advent of a range of new fluorescent probes used to tag proteins or molecules of interest, including the nontoxic, green fluorescent protein (GFP). While fluorescence microscopes permit the collection of large, high-dimensional data sets, their manual processing is inefficient, not reproducible, time-consuming and error-prone, prompting the movement towards automated, efficient and robust processing for high-throughput applications. Segmentation, a fundamental, yet very difficult problem in image processing, is often the first processing step following acquisition. While it is always desirable for imaging tasks in biology to be as automated as possible, this is especially critical for segmentation, as it takes human experts anywhere from hours to days to segment by hand. The current segmentation algorithm used in fluorescence microscopy - the watershed algorithm - is not well-suited to this problem. Meanwhile, state-of-the-art segmentation algorithms have only recently begun to be applied to this problem. We will work both on a specific biological problem of Golgi study, as well as other fluorescence microscope data sets provided by our collaborators. Thus: We propose to develop a flexible framework, a family of algorithms and a software toolbox for the automated segmentation of fluorescence microscope images based on multiscale transformations and active contour methods. We plan on pursuing this goal through the following three specific aims: 7 Specific Aim M: Develop a class of multiscale active contour transformations to efficiently extract those features of the fluorescence microscope data needed for segmentation and develop a class of energy functionals and a corresponding family of segmentation algorithms that is flexible, modular and has an efficient implementation. 7 Specific Aim D: Develop different algorithmic modules to cater to data-specific issues pertaining to initialization, computation of the forces, topology preservation and multiresolution transformation, and nature of the data such as multidimensionality/tissue images, as well as auxiliary modules specific to the application. 7 Specific Aim S: Develop a flexible software platform and a user-friendly GUI to facilitate use by biologists as well as interaction between biologists and algorithm developers. The motivation is for this family of algorithms to be used for segmentation of fluorescence microscope data sets, as these are widely used to study processes at molecular and cellular levels. As segmentation is a typical first step in the analysis of such data sets, robust and automated segmentation algorithms are a must to enable large-scale studies of molecular and cellular processes.

描述（由申请人提供）：近年来，生物科学的焦点已转向理解细胞和分子水平上的复杂系统，荧光显微镜极大地促进了这项任务。它的成功部分归功于一系列用于标记感兴趣的蛋白质或分子的新型荧光探针的出现，包括无毒的绿色荧光蛋白（GFP）。虽然荧光显微镜可以收集大型、高维数据集，但其手动处理效率低下、不可重复、耗时且容易出错，从而促使高通量应用转向自动化、高效和稳健的处理。分割是图像处理中的一个基本但非常困难的问题，通常是采集后的第一个处理步骤。虽然生物学中的成像任务总是希望尽可能自动化，但这对于分割尤其重要，因为人类专家需要花费数小时到数天的时间来手动分割。当前荧光显微镜中使用的分割算法 - 分水岭算法 - 不太适合这个问题。与此同时，最先进的分割算法最近才开始应用于这个问题。我们将致力于研究高尔基体研究的特定生物学问题，以及我们的合作者提供的其他荧光显微镜数据集。因此：我们建议开发一个灵活的框架、一系列算法和软件工具箱，用于基于多尺度变换和主动轮廓方法自动分割荧光显微镜图像。我们计划通过以下三个具体目标来实现这一目标： 7 具体目标 M：开发一类多尺度主动轮廓变换，以有效提取分割所需的荧光显微镜数据特征，并开发一类能量泛函和相应的族灵活、模块化且具有高效实现的分割算法。 7 具体目标 D：开发不同的算法模块，以满足与初始化、力计算、拓扑保存和多分辨率变换以及数据性质（例如多维/组织图像）相关的数据特定问题，以及特定于该应用程序。 7 具体目标 S：开发灵活的软件平台和用户友好的 GUI，以方便生物学家使用以及生物学家和算法开发人员之间的交互。该算法系列的动机是用于荧光显微镜数据集的分割，因为这些算法被广泛用于研究分子和细胞水平的过程。由于分割是分析此类数据集的典型第一步，因此强大且自动的分割算法是实现分子和细胞过程大规模研究所必需的。