Automatic Quantification of High S:N Images Using VCS

使用 VCS 自动量化高 S:N 图像

基本信息

批准号：
6694953
负责人：
PETER Randolph MOUTON
金额：
$ 9.88万
依托单位：
STEREOLOGY RESOURCE CENTER, INC.
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-07-10 至 2004-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6694953
关键词：
animal tissue automated data processing bioimaging /biomedical imaging computer human interaction computer system design /evaluation data collection human tissue image processing method development microscopy

项目摘要

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.

描述（由申请人提供）：理论上无偏见的体视学已应用于 Stereologer（一种市售的集成硬件软件显微镜系统）中生物组织切片的形态测量分析。该系统和其他此类系统的一般局限性在于，由经过培训的非专家用户识别目标对象需要时间和劳动力密集型的要求。第一阶段 SBIR 将测试一种新颖修改的准确性、效率和可行性 - 经验证的计算机立体分析 (VCS) - 用于自动量化具有高信噪比 (S:N) 的物体的体视参数。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估：自动阶段控制下的系统随机采样；并且，自动量化平均物体尺寸和变化。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 > 90% 的最小 S:N。将通过对常规组织切片进行并行自动和半自动分析以及外部 Beta 测试站点来评估准确性。 VCS 方法有可能在不损失准确性的情况下大幅提高体视学研究的通量，从而刺激广泛的生物医学研究项目的进展。