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被应用于Stereologer，一个商业上可获得的，集成的硬件-软件-显微镜系统中的生物组织切片的形态测定分析。这种系统和其他这种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。这第一阶段SBIR将测试的准确性，效率和可行性的一种新的修改-验证的计算机立体分析（ESTA）-自动量化的体视学参数的对象具有高的信噪比（S：N）。这种图像分析和无偏体视学的自动组合将在Stereologer系统上实现，并通过两步过程进行评价：自动阶段控制下的系统随机采样;以及平均物体尺寸和变化的自动量化。为避免收集虚假数据，第一步之后的核查过程将确保在自动数据收集之前存在等于或> 90%的最小S：N。将通过对常规组织切片的平行自动和半自动分析以及外部β检测中心评价准确度。该方法有可能大大提高体视学研究的吞吐量，而不损失准确性，从而刺激在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.