Survey statistics of automated segmentations applied to optical imaging of mammalian cells.
Survey statistics of automated segmentations applied to optical imaging of mammalian cells.
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DOI：
10.1186/s12859-015-0762-2
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发表时间：
2015-10-15
影响因子：
3
通讯作者：
Brady M
中科院分区：
文献类型：
--
作者：
Bajcsy P;Cardone A;Chalfoun J;Halter M;Juba D;Kociolek M;Majurski M;Peskin A;Simon C;Simon M;Vandecreme A;Brady M
这篇调查论文的目的是概述细胞测量使用光学显微镜成像，然后自动图像分割。主要感兴趣的细胞测量取自哺乳动物细胞及其组分。它们被表示为生物学感兴趣的二维或三维（2D或3D）图像对象。在我们的应用中，这样的细胞测量对于理解细胞现象（例如细胞计数、细胞-支架相互作用、细胞集落生长速率或细胞多能性稳定性）以及对于建立干细胞疗法的质量度量是重要的。在这种情况下，这篇调查论文的重点是自动分割作为一种基于软件的测量，导致定量细胞测量。我们首先定义了本次调查的范围和分类模式。接下来，根据主要类别对所有找到的和手动筛选的出版物进行分类：（1）感兴趣的对象（或待分割的对象），（2）成像模态，（3）数字数据轴，（4）分割算法，（5）分割评估，（6）用于分割加速的计算硬件平台，以及（7）对象（细胞）测量。最后，所有的分类文件被编程转换成一组超链接的网页与发生和共现统计分配的类别。调查报告向读者介绍：（a）关于应用于哺乳动物细胞的光学显微镜成像的自动分割的已发表论文的最新综述，（B）在细胞光学成像的背景下的分割方面的分类，（c）关于细胞测量、分割、分割对象、分割评估的直方图和共现概括统计，以及使用计算平台来加速分割执行，以及（d）要追求的开放研究问题。这篇调查论文的新贡献是：（1）一种新型的细胞测量和自动分割分类，（2）关于已发表文献的统计数据，以及（3）在https://isg.nist.gov/deepzoomweb/resources/survey/index.html上提供了一个网络超链接界面，用于调查论文的分类统计数据。
The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.