Associative image analysis: A method for automated quantification of 3D multi-parameter images of brain tissue
Associative image analysis: A method for automated quantification of 3D multi-parameter images of brain tissue
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DOI：
10.1016/j.jneumeth.2007.12.024
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发表时间：
2008-05-15
影响因子：
3
通讯作者：
Roysam, Badrinath
中科院分区：
文献类型：
--
作者：
Bjornsson, Christopher S.;Lin, Gang;Roysam, Badrinath
大脑结构的复杂性已经混淆了先前的努力，以提取定量的基于图像的测量。我们提出了一个系统的“分而治之”的方法来分析脑组织的三维（3D）多参数图像，描绘和分类的关键结构，并计算它们之间的定量关联。为了证明该方法，厚（类似于100 μ m）切片的大鼠脑组织标记使用三至五个荧光信号，并使用光谱共聚焦显微镜和unmixing算法成像。使用自动3D分割和跟踪算法来描绘细胞核、脉管系统和细胞过程。根据这些分割，为每个细胞计算一组23个内在和8个关联的基于图像的测量。这些特征被用于分类星形胶质细胞、小胶质细胞、神经元和内皮细胞。计算细胞之间以及细胞与脉管系统之间的关联，并表示为图形网络以进行进一步分析。使用图形界面对自动化结果进行验证，该界面允许研究人员检查和纠正3D中每个单元格的编辑。核计数准确度> 89%，细胞分类准确度范围为81 - 92%，取决于细胞类型。我们提出了一个名为FARSIGHT实现我们的方法的软件系统。它的输出是一个详细的XML文件，其中包含可用于中枢神经系统的各种定量假设驱动和探索性研究的测量结果。(c)2008 Elsevier B. V.保留所有权利。
Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (similar to 100 mu m) slices of rat brain tissue were labeled using three to five fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was > 89%, and cell classification accuracy ranged from 81 to 92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. (c) 2008 Elsevier B.V. All rights reserved.