Bjornsson Christopher S, Lin Gang, Al-Kofahi Yousef, Narayanaswamy Arunachalam, Smith Karen L, Shain William, Roysam Badrinath
Center for Neural Communication Technology, New York State Department of Health, Wadsworth Center, Albany, NY 12201-0509, USA.
J Neurosci Methods. 2008 May 15;170(1):165-78. doi: 10.1016/j.jneumeth.2007.12.024. Epub 2008 Jan 17.
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 ( approximately 100 microm) 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.
脑结构的复杂性使得之前基于图像提取定量测量值的努力受到阻碍。我们提出了一种系统的“分而治之”方法，用于分析脑组织的三维（3D）多参数图像，以描绘和分类关键结构，并计算它们之间的定量关联。为了演示该方法，使用三到五种荧光信号对大鼠脑组织的厚切片（约100微米）进行标记，并使用光谱共聚焦显微镜和去混合算法进行成像。使用自动3D分割和追踪算法来描绘细胞核、脉管系统和细胞突起。从这些分割结果中，为每个细胞计算了一组23个内在的和8个基于关联图像的测量值。这些特征被用于对星形胶质细胞、小胶质细胞、神经元和内皮细胞进行分类。计算细胞之间以及细胞与脉管系统之间的关联，并将其表示为图形网络，以便进行进一步分析。使用图形界面验证自动结果，该界面允许研究人员在3D中检查和校正每个细胞。核计数准确率>89%，细胞分类准确率根据细胞类型在81%至92%之间。我们提出了一个名为FARSIGHT的软件系统来实现我们的方法。其输出是一个详细的XML文件，包含可用于中枢神经系统各种定量假设驱动和探索性研究的测量值。
