AUTOMATED SEGMENTATION OF FLUORESCENCE MICROSCOPY DATA SETS

描述（由申请人提供）：近年来，生物科学的重点已经转移到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新的荧光探针的出现，这些探针用于标记感兴趣的蛋白质或分子，包括无毒的绿色荧光蛋白（GFP）。虽然荧光显微镜允许收集大型高维数据集，但其手动处理效率低下，不可重现，耗时且容易出错，促使其向高通量应用的自动化，高效和稳健处理方向发展。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，因为人工分割需要几个小时到几天的时间。目前的分割算法中使用的荧光显微镜-分水岭算法-是不是很适合这个问题。同时，国家的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。由此可见：我们建议开发一个灵活的框架，一个家庭的算法和一个软件工具箱的荧光显微镜图像的自动分割的基础上多尺度变换和活动轮廓方法。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：开发一类多尺度活动轮廓变换，以有效地提取分割所需的荧光显微镜数据的特征，并开发一类能量泛函和相应的分割算法家族，该算法是灵活的，模块化的，并具有高效的实现。7具体目标D：开发不同的算法模块，以满足与初始化、力的计算、拓扑保持和多分辨率转换以及数据的性质（如多维/组织图像）有关的数据特定问题，以及特定于应用程序的辅助模块。7具体目标S：开发一个灵活的软件平台和用户友好的GUI，以方便生物学家的使用以及生物学家和算法开发人员之间的互动。动机是这个家庭的算法被用于荧光显微镜数据集的分割，因为这些被广泛用于在分子和细胞水平上的研究过程。由于分割是分析此类数据集的典型第一步，因此必须使用强大的自动分割算法才能进行大规模的分子和细胞过程研究。

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.