Collaborative Systems for Analyzing Tissue Microarrays

描述(由申请人提供)： 组织微阵列技术在减少与组织库、蛋白质组学和结果研究相关的研究的时间和成本方面具有巨大的潜力。然而，捕获、组织、更新、交换和分析由该技术生成的数据会带来许多重大挑战。即使是涉及组织微阵列的有限研究产生的大量数据、文本和图像，随着时间的推移也可以迅速接近一个小型临床部门的数据、文本和图像。现代研究的几个领域的大型项目往往涉及多个机构的努力，其中研究人员和资源分布在多个校园、城市和州，这一事实加剧了这一挑战。这些研究领域今后的进展将取决于来自不同地点的个人互动、分享和吸收来自各种来源和模式的信息的能力。这项赠款提案的中心目标是开发一种自动化组织微阵列分析系统，其特点是在协作环境中具有分布式成像和数据管理能力，以支持病理学和肿瘤学中的即兴远程医疗应用。新泽西医学与牙科大学将与新泽西癌症研究所、罗格斯大学和宾夕法尼亚大学医学院合作开发算法、计算工具和技术，使来自不同研究和临床地点的个人能够在多用户环境中可靠地对组织微阵列、病理标本和相关数据进行成像、分析、存档和共享，以支持协作的临床和研究活动。该项目的目标是(1)设计、开发和评估一套便携式软件工具，用于在多用户环境中执行组织微阵列的无监督成像和存档；(2)评估和优化一组新开发的用于癌组织阵列表达定量表征的颜色分解算法的性能；(3)研究可变带宽均值漂移算法用于分割组成阵列圆盘的使用；(4)研究基于颜色活动轮廓模型和稳健估计的新算法在亚细胞定位中的使用，并与均值漂移结果进行比较；(5)研究文本库用于表征癌症组织阵列中的表达模式的使用；(6)开发组织微阵列储存库(TMR)子系统，该子系统使来自不同研究和临床站点的个人能够用包括相关图像指标和成像阵列的新病例填充数据库；(7)将(1-5)的结果集成到自动TMA分析系统的可靠图像处理模块中；(8)将具有网络功能的TMA分析服务器和客户端部署到UMDNJ、UPenn和癌症研究所的战略站点，并在实际操作条件下评估多用户、多机构环境中的表现；以及(7)根据性能研究改进系统，并向临床和研究界提供支持网络的软件和计算工具以及支持文件。

DESCRIPTION (provided by applicant): Tissue microarray technology holds great potential for reducing the time and cost associated with conducting research in tissue banking, proteomics, and outcome studies. However, capturing, organizing, updating, exchanging, and analyzing the data generated by this technology creates a number of significant challenges. The sheer volume of data, text, and images arising from even limited studies involving tissue microarrays can over time quickly approach those of a small clinical department. This challenge is compounded by the fact that large-scale projects in several areas of modern research often involve multi-institutional efforts in which investigators and resources are spread out over multiple campuses, cities, and states. Future progress in these areas of research will depend on the capacity of individuals from disparate sites to interact, share and assimilate information derived from a variety of sources and modalities. The central objective of this grant proposal is to develop an automated tissue microarray analysis system which features distributed imaging and data management capability in a collaborative environment which supports impromptu telemedicine applications in pathology and oncology. The University of Medicine & Dentistry of New Jersey will collaborate with the Cancer Institute of New Jersey, Rutgers University and the University of Pennsylvania School of Medicine to develop the algorithms, computational tools, and technologies which enable individuals from disparate research and clinical sites to reliably image, analyze, archive and share tissue microarrays, pathology specimens, and correlated data in multi-user environments in order to support collaborative clinical and research activities. The aims of the proposed project are (1) to design, develop, and evaluate a portable suite of software tools for performing unsupervised imaging and archiving of tissue microarrays in multi-user environments; (2) to evaluate and optimize the performance of a set of newly developed color decomposition algorithms for use in quantitative characterization of expression in cancer tissue arrays; (3) to investigate the use of a variable bandwidth mean shift algorithm for segmenting constituent array discs; (4) to investigate the use of a novel algorithm based on color active contour models and robust estimation for use in sub-cellular localization and compare with mean shift results; (5) To investigate the use of texton libraries for characterizing expression patterns in cancer tissue arrays; (6) to develop a Tissue Microarray Repository (TMR) subsystem which enables individuals from disparate research and clinical sites to populate the database with new cases including correlated image metrics and imaged arrays; (7) to integrate the results from (1-5) into a reliable image processing module of the automated TMA analysis system; (8) to deploy the web-enabled TMA analysis server and client to strategic sites throughout UMDNJ, UPenn, and the Cancer Institute and evaluate performance in multi-user, multi institutional environments under real operating conditions; and (7) to refine the system based on performance studies and make web-enabled software and computational tools and supporting documentation available to the clinical and research communities.