Phase II: Robust analysis of subcellular time-lapse assays

描述（由申请人提供）：该项目的目标是开发和商业化下一代活细胞，延时显微镜图像识别软件，专门用于高通量定量亚细胞功能。该软件集成了当前信息学工具中不可用的亚细胞延时分析和建模的新颖而强大的方法，用于增强信号检测和抗噪性，以显着提高分析通量，准确性，效率和可靠性。这些包括三个层次的分析工具：1）鲁棒对象检测通过使用置信图进行像素与对象的非二进制概率关联来增强对象检测; 2）鲁棒特征优化通过利用整个图像或电影内的时空信息来细化置信图或对置信图进行加权以用于模型拟合来增强定量特征测量;以及3）结果导向模型拟合通过具有内置可靠性评估和使用时空图像信息的误差校正的迭代拟合来增强测定模型参数。该阶段II项目的目标是将这些技术结合到SVCellTM平台中，在更广泛的新亚细胞检测中概括和表征其性能，并通过我们的商业合作伙伴为产品发布准备整个平台。第二阶段的可交付成果将是为基础和药物发现科学家提供的市场就绪软件包。我们的具体目标是：1）优化和验证广泛生物测定应用的亚细胞分析模块; 2）SVCell中亚细胞分析模块的产品软件工程;以及3）通过现场测试和科学合作评估SVCell beta的产品就绪性。在固定和活细胞中观察亚细胞表型的成像分析处于生命科学成像研究的前沿。它们为研究人员提供了新的工具，以极大的分辨率剖析细胞功能的机制。它们带来了新的见解和新的发现，可以对所有基础研究产生重大影响。这些新的检测方法可以快速扩展并转化为成像屏幕，用于使用SVCell平台进行药物或生物发现和疾病诊断。总的来说，这个第二阶段的项目有望通过提高基础研究的速度和效率，高通量成像检测开发和部署具有微妙表型的新型高通量成像检测，对人类健康产生重大影响。显微图像识别软件有望通过提高基础研究的准确性、速度和效率、高通量成像检测开发以及部署具有微妙表型的新型高通量成像检测来对人类健康产生重大影响。它将为研究人员提供一种新的工具，以极大的分辨率剖析细胞功能的机制。

DESCRIPTION (provided by applicant): The goal of this project is to develop and commercialize next generation live cell, time-lapse microscopy image recognition software specialized for high throughput quantification of subcellular functions. The software integrates novel and robust methods of subcellular time-lapse analysis and modeling not available in the current informatics tools for the enhancement of signal detection and noise immunity to significantly improve on assay throughput, accuracy, efficiency, and reliability. These include three levels of analysis tools: 1) robust object detection enhances object detection by making a non-binary, probabilistic association of pixels to objects using confidence maps; 2) robust feature optimization enhances quantitative feature measurement by utilizing the spatial temporal information within the entire image or movie to refine the confidence maps or weight them for model fitting; and 3) outcome directed model fitting enhances the assay model parameter through iterative fitting with built-in reliability assessment and error correction using spatial-temporal image information The goal of this phase II project is to incorporate these technologies into the SVCell(tm) platform, generalize and characterize their performance in a wider range of new subcellular assays, and prepare the entire platform for product release through our commercial partners. The phase II deliverables will be a market ready software package for basic and drug discovery scientists. Our specific aims are: 1) Optimize and validate the subcellular analysis module for broad bio-assay application; 2) Product software engineering of the subcellular analysis module in SVCell; and 3) Evaluate the product readiness of the SVCell beta through field tests and scientific collaborations. Imaging assays looking at subcellular phenotypes in both fixed and live cells are at the cutting edge of life science imaging research. They provide researchers with new tools to dissect the mechanisms of cellular function with great resolution. They lead to new insights and new discoveries that can have significant impact across all of basic research. These new assays can be rapidly scaled and translated into imaging screens for drug or biological discovery and disease diagnosis using the SVCell platform. Overall this phase II project promises to make a significant impact on human health by increasing the speed and efficiency of basic research, high throughput imaging assay development, and deployment of novel high throughput imaging assays with subtle phenotypes. Microscopy image recognition software promises to make a significant impact on human health by increasing the accuracy, speed and efficiency of basic research, high throughput imaging assay development, and deployment of novel high throughput imaging assays with subtle phenotypes. It will provide researchers with a new tool to dissect the mechanisms of cellular function with great resolution.