Video Assisted Islet Cell Isolation System

描述(由申请人提供)： 胰岛细胞移植为100多万患有胰岛素依赖型糖尿病的美国人提供了治愈的希望。然而，目前的移植结果高度依赖于从供体器官中分离和纯化胰岛过程中所涉及的医生、科学家和技术人员的技能。为了帮助减少当前方法固有的巨大变异性和人力，Emerging Concepts，Inc.与辛辛那提大学和Sheet Dynamic，Ltd.合作，正在开发一种胰岛细胞分离系统，以自动化分离过程中的主要步骤。该系统有望标准化和降低过程的可变性，并将允许优化胰岛细胞的产量、活力和纯度。目前，该系统自动化了过程中的多个关键步骤，但仍需要训练有素的技术人员来确定关键过程变量之一，即细胞分离阶段的终点。高质量的自动化视频分析系统可以通过改善这一关键决策点来显著改善固有的过程可变性。因此，这一阶段可行性研究的重点是开发这样一个视频系统所需的必要的数字图像处理和分析软件。 这一建议有三个具体目标：(1)开发胰岛细胞分离分离阶段的细胞图像和替代结果的数据库；(2)开发图像分析算法以区分胰岛和外分泌组织，从分割的图像中提取关键特征和指标，并建立预测模型，将这些指标与从替代结果得出的最佳分离停止时间相关联；以及(3)建立虚拟显微镜(网络门户)，并收集对第一个目标中产生的图像序列的数据库进行操作的胰岛细胞移植专家的反应。如果预测模型的性能能够被证明等于或好于(a=0.05)基于相同数据的专家小组，则该阶段研究将被认为是成功的。

DESCRIPTION (provided by applicant): Islet cell transplantation offers the hope of a cure for the more than 1 million Americans who are type I (insulin dependant) diabetics. However, current transplantation outcomes are highly dependent on the skill of the physicians, scientists, and technicians involved in the process of isolating and purifying islets from donor organs. To help reduce the large variabilities and manpower inherent in the current methods, Emerging Concepts, Inc., in collaboration with The University of Cincinnati and Sheet Dynamics, Ltd., is developing an islet cell isolation system to automate the major steps in the isolation process. The system holds promise for standardizing and reducing process variability, and will allow for the optimization of yield, viability, and purity of the islet cells. Currently, the system automates multiple key steps in the process, but still requires a trained technician to determine one of the critical process variables, namely, the end point of the cell dissociation stage. A high quality, automated video analysis system could significantly improve the inherent process variability by improving this key decision point. Therefore, the focus of this Phase I feasibility study is to develop the necessary digital image processing and analysis software needed for such a video system. This proposal has three specific aims: (1) develop a database of cell images and surrogate outcomes during the dissociation phase of islet cell isolation; (2) develop image analysis algorithms to distinguish between islets and exocrine tissue, extract key features and metrics from the segmented images, and formulate a predictive model that relates these metrics to optimal dissociation halting times derived from the surrogate outcomes; and (3) build a virtual microscope (a web portal) and collect responses from islet cell transplantation experts operating on the database of image sequences generated in the first aim. This Phase study will be considered a success if the performance of the predictive model can be shown to be equal to or better than (a=.05) the panel of experts operating on the same data.