AUTOMATED SEGMENTATION OF FLUORESCENCE MICROSCOPY DATA SETS

描述(申请人提供)：近年来，生物科学的重点已经转移到在细胞和分子水平上理解复杂系统，这项任务在荧光显微镜下得到了极大的便利。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、不可重复性、耗时且容易出错，促使人们转向自动化、高效和强大的处理，以实现高通量应用。分割是图像处理中的一个基本问题，也是一个非常困难的问题，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地自动化，但这对分割尤其关键，因为无论在哪里，人类专家都需要几个小时到几天的时间来手工分割。目前用于荧光显微镜的分割算法-分水岭算法-不太适合这个问题。与此同时，最先进的分割算法直到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一个特定生物学问题，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、一系列算法和一个软件工具箱，用于基于多尺度变换和活动轮廓法的荧光显微镜图像自动分割。我们计划通过以下三个具体目标来追求这一目标：7具体目标M：开发一类多尺度活动轮廓变换来有效地提取分割所需的荧光显微镜数据的特征，并开发一类灵活、模块化且具有高效实现的能量泛函和相应的分割算法族。7具体目标D：开发不同的算法模块，以处理与初始化、力的计算、拓扑保持和多分辨率变换、数据的性质(如多维/组织图像)有关的具体数据问题，以及针对具体应用的辅助模块。7具体目标S：开发灵活的软件平台和用户友好的图形用户界面，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。其动机是将这一系列算法用于荧光显微镜数据集的分割，因为这些算法广泛用于在分子和细胞水平上研究过程。由于分割是分析这类数据集的典型的第一步，为了能够对分子和细胞过程进行大规模研究，稳健和自动化的分割算法是必须的。

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.