A spatio-temporally integrated and nonlinear particle tracking system for live cell imaging

用于活细胞成像的时空集成非线性粒子跟踪系统

• 批准号: EP/F019165/1
• 负责人: Ilan Davis
• 金额: $ 34.29万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF019165%2F1
• 关键词: spatio; temporally; integrated; nonlinear; particle

Despite recent advances in light microscopy revolutionizing live cell imaging [1-2], the full potential of the increasing high sensitivity and resolution of modern microscopes has yet to be realised. A key barrier is the limited power and scope of image analysis techniques currently available to cell biologists. Although commercial programs promise automated particle identification and tracking and have been successfully used to assist image analysis in high signal to noise ratio environments, our extensive experimental tests have shown that they are inadequate to deal with live cell images that are of low signal to noise ratio, poor and variable contrast, and often comprise multiple particles in close proximity and with inconsistent movement. In many cases, particles can only be tracked manually by placing the cursor on objects over time. A major improvement would be to exploit advanced image processing and analysis algorithms to deal with complex time-lapse live cell data, in much the same way that new de-convolution algorithms have significantly improved wide-field imaging in Biology [3]. In this project, we propose to research and develop a nonlinear partial differential equation (PDE) method as a new approach to tracking biological particles in live cells. A key advantage of this method over all commercial software currently available to biologists is to make full use of temporal and spatial relationships in time-lapse data to assist in overcoming severe noise effects and recognition of targets in real biological conditions. Specifically, we will develop a spatio-temporally integrated and nonlinear particle tracking system based on the PDE approach and integrate it to the ImageJ image analysis suite [4] to provide a user-friendly graphical interface to biologists.

尽管光学显微镜的最新进展彻底改变了活细胞成像[1-2]，但现代显微镜越来越高的灵敏度和分辨率的全部潜力尚未实现。一个关键的障碍是目前细胞生物学家可用的图像分析技术的能力和范围有限。虽然商业程序承诺自动颗粒识别和跟踪，并已成功地用于协助图像分析在高信噪比的环境中，我们广泛的实验测试表明，他们是不足以处理活细胞图像的低信噪比，差和可变的对比度，并经常包括多个粒子在紧密接近和不一致的运动。在许多情况下，只能通过将光标放置在对象上来手动跟踪粒子。一个主要的改进将是利用先进的图像处理和分析算法来处理复杂的延时活细胞数据，与新的去卷积算法显着改善生物学中的宽视场成像的方式大致相同[3]。在本计画中，我们提出研究与发展一种非线性偏微分方程（PDE）方法，作为追踪活细胞中生物粒子的新方法。这种方法的一个关键优势，目前所有的商业软件提供给生物学家是充分利用时间和空间关系的时间推移数据，以帮助克服严重的噪声影响和识别的目标在真实的生物条件。具体来说，我们将开发一个基于PDE方法的时空集成和非线性粒子跟踪系统，并将其集成到ImageJ图像分析套件[4]中，为生物学家提供用户友好的图形界面。

项目成果

An adaptive non-local means filter for denoising live-cell images and improving particle detection.

• DOI: 10.1016/j.jsb.2010.06.019
• 发表时间: 2010-12
• 期刊: JOURNAL OF STRUCTURAL BIOLOGY
• 影响因子: 3
• 作者: Yang, Lei;Parton, Richard;Ball, Graeme;Qiu, Zhen;Greenaway, Alan H.;Davis, Ilan;Lu, Weiping
• 通讯作者: Lu, Weiping