Automatic Stereology of Biological Tissue Using 3-D VCS

描述（由申请人提供）：II期研究继续了I期开始的工作，为市售体视学系统（Stereologer）配备图像分析功能，以自动采样和量化组织切片中的生物学特征。研究人员已经将部分由合作者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，称为验证计算机化立体分析（Verified Computerized Stereoanalysis，简称CSTR）。第一阶段证明了定量的能力，在组织切片中的增殖细胞的大小参数与金标准（手动点击）的方法相同的准确性，但与一个显着的8倍的吞吐量效率的提高。在操作上，当用户从研究中的初始病例开始对第一部分执行手动数据收集时，该EEG程序获取与感兴趣特征相关联的彩色像素的内部目标。一旦用户验证了相对于手动数据收集的可接受的准确度水平（黄金标准），程序可以切换到全自动模式，即系统随机采样的电动载物台控制的组合;感兴趣的染色显微特征的自动检测;以及基于最先进的体视学原理的全局和局部尺寸参数及其变化的自动分析。这些研究发现，用特定的基于免疫学的探针标记（染色）蛋白质的生物特征，然后用色原反应或荧光放大信号，刺激了来自ESTA算法的最稳健的响应。第二阶段将扩展到所有一阶的三维分析（数量、长度、表面积和体积）和二阶（变异、空间分布）体视学参数（目标1）;对照金标准方法验证3-D染色，并确定主要的组织处理和染色程序，以提高算法稳健性（目标2）;开发立体测量和在线文档，以帮助计算机化体视学系统的当前用户从手动方法转换为自动测量方法（目标3）。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;减少时间和劳动力方面的研究成本;并加速科学进步，改善健康和疾病管理。过去十年来，Stereologer和其他体视学资源在全球范围内的销售和支持证明了PI及其同事能够成功地将III期的立体成像项目商业化的确凿证据。

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.