Automatic Quantification of High S:N Images Using VCS

描述(由申请人提供)： 理论上无偏的体视学已经被应用于立体仪中的生物组织切片的形态计量分析，这是一种商用的集成硬件-软件-显微镜系统。该系统和其他此类系统的一般限制在于由受过训练的非专家用户识别目标对象的时间和劳动密集型要求。这一阶段的SBIR将测试一种新的改进--经过验证的计算机化立体分析(VCS)--的准确性、效率和可行性，用于自动量化高信噪比(S：N)对象的体视学参数。这种图像分析和无偏立体学的自动组合将在立体仪系统上实施，并通过两个步骤进行评估：自动舞台控制下的系统随机抽样；以及平均对象大小和变化的自动量化。为了避免收集虚假数据，第一步之后的验证过程将确保在自动数据收集之前存在等于或&gt；90%的最小S：N。准确度将通过对常规组织切片和外部测试点的并行自动和半自动分析进行评估。VCS方法有可能在不损失精度的情况下显著提高体视学研究的吞吐量，从而刺激广泛的生物医学研究项目的进展。

DESCRIPTION (provided by applicant): Theoretically unbiased stereology has been applied to the morphometric analysis of biological tissue sections in the Stereologer, a commercially available, integrated hardware-software-microscopy system. A general limitation of this and other such systems lies in the time- and labor-intensive requirement for identification of targeted objects by trained non-expert users. This Phase 1 SBIR will test the accuracy, efficiency, and feasibility of a novel modification -- Verified Computerized Stereoanalysis (VCS) - for automatic quantification of stereological parameters for objects with a high signal-to-noise ratio (S:N). This automatic combination of image analysis and unbiased stereology will be implemented on the Stereologer system and evaluated through a two-step process: systematic-random sampling under automatic stage control; and, automatic quantification of mean object size and variation. To avoid the collection of spurious data, a verification process after step one will ensure that minimal S:N of equal to or > 90% exists prior to automatic data collection. Accuracy will be evaluated through parallel automatic and semi-automatic analyses on routine tissue sections and by external beta test sites. The VCS approach has the potential to drastically improve throughput of stereological studies, without a loss of accuracy, and thereby stimulate progress in a wide range of biomedical research projects.