Automatic Stereology of Biological Tissue Using 3-D VCS

描述（由申请人提供）：第二阶段的研究继续第一阶段开始的工作，为商用立体系统（Stereologer）配备具有图像分析能力的图像分析系统，以自动采样和量化组织切片中生物感兴趣的特征。研究人员已经将部分由合作伙伴的导弹弹道系统研究计划开发的图像分析算法改编为自动检测和自动分析程序，称为验证计算机立体分析（VCS）。第一阶段证明了VCS量化组织切片中增殖细胞大小参数的能力，其准确性与金标准（手动点击）方法相同，但吞吐量效率显著提高了8倍。在操作上，VCS程序获取与感兴趣的特征相关的彩色像素的内部目标，而用户则从研究中的初始案例中手动收集第一部分的数据。一旦用户验证了相对于手动数据收集（黄金标准）的可接受的精度水平，程序可以切换到全自动模式，结合电动阶段控制系统随机抽样；感兴趣染色显微特征的自动检测；基于最先进的立体学原理，自动分析全局和局部尺寸参数及其变化。这些研究发现，用特异性免疫探针标记（染色）蛋白质的生物学特征，然后用显色反应或荧光放大信号，刺激了VCS算法最强大的反应。第二阶段将把VCS扩展到所有一阶（数量、长度、表面积和体积）和二阶（变化、空间分布）立体参数的三维分析（Aim 1）；根据金标准方法验证3-D VCS，并确定主要的组织处理和染色程序，以增加算法的鲁棒性（目标2）；并开发立体教程和联机文件，以协助计算机立体系统的当前用户从手动转向自动VCS方法（目标3）。VCS的长期目标是在不损失准确性的情况下提高组织切片参数立体分析的吞吐量效率；减少时间和人力方面的研究成本；加快改善健康和疾病管理的科学进步。在过去的十年里，Stereologer和其他立体学资源在全球范围内的销售和支持证明了PI及其同事可以在第三阶段成功地将VCS项目商业化。

DESCRIPTION (provided by applicant): The studies in Phase II continue the effort begun in Phase I to equip a commercially available stereology system (Stereologer) with image analysis capability to automatically sample and quantify features of biological interest in tissue sections. The investigators have adapted image analysis algorithms, developed in part by a collaborator's missile ballistic systems research program, into an auto-detection and auto-analysis program called Verified Computerized Stereoanalysis (VCS). Phase I demonstrated the ability of VCS to quantify size parameters of proliferating cells in tissue sections with equal accuracy to the gold standard (manual click) approach, but with a significant 8-fold improvement in throughput efficiency. Operationally, the VCS program acquires an internal target of color pixels associated with the feature of interest, while the user performs manual data collection for the first section from the initial case in the study. Once the user verifies an acceptable level of accuracy relative to manual data collection (gold standard), the program can be switched into fully automatic mode, a combination of motorized stage control for systematic-random sampling; auto-detection of stained microscopic features of interest; and, auto-analysis of global and local size parameters and their variation based on state-of-the-art stereological principles. These investigations found that biological features that label (stain) proteins with specific immunological-based probes, followed by amplification of the signal with chromogen reactions or fluorescence, stimulate the most robust response from the VCS algorithm. Phase II will expand VCS to the 3-D analysis of all 1st-order (number, length, surface area, and volume) and 2nd-order (variation, spatial distribution) stereological parameters (Aim 1); validate 3-D VCS against the gold standard approach and identify the principal tissue processing and staining procedures to increase algorithm robustness (Aim 2); and, develop StereoTutorials and on-line documentation to assist the conversion of current users of computerized stereology systems from manual to automatic VCS approaches (Aim 3). The long-term goal for VCS is to increase the throughput efficiency for stereoanalyses of parameters on tissue sections without a loss of accuracy; reduce research costs in terms of time and labor; and, accelerate scientific progress toward improvements in health and the management of disease. Solid evidence that the PI and colleagues can successfully commercialize the VCS program in Phase III is demonstrated by worldwide sales and support of the Stereologer and other stereology resources for the past decade.