Combination of Fast, Robust TV-based Deconvolution with Variational Segmentation for Microscopic Data

快速、鲁棒的基于电视的反卷积与微观数据的变分分割相结合

• 批准号: 14024299
• 负责人: Professor Dr. Jürgen Hesser
• 金额: --
• 依托单位: Institut für Technische Informatik (ZITI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2005
• 资助国家: 德国
• 起止时间: 2004-12-31 至 2010-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/14024299?language=en
• 关键词: Combination; Fast; Robust; TV; based

Quantitative measurements in biology using microscopy allow understanding of cellfunctions. The necessary basis is the reliability and sensitivity of such a measurement in the case of low photon counts since a low photon count is required in order to avoid photo bleaching or damages to the cell (phototoxity). In this project, we will implement such a robust measurement system for fluorescent microscopy. It will for the first time combine optimal methods for PSF estimation, segmentation, and deconvolution. We will develop a method for PSF estimation using Zernike polynomials that are adapted to both, the degrees of freedom of physically feasible PSFs, and the physical noise model of the system. We extend this method by using a combined segmentation-blind deconvolution approach, restricting the PSF estimation to areas where we segment objects of known structure properties. This combination should not only lead to better results, we will also overcome known limitations of blind deconvolution, i.e. the insensitivity to the PSF kernel. To make the system applicable in practice, new approaches to speed up computations are implemented, e.g. using tube methods, adaptive restoration and ordered subsets. We expect a significant improvement in accuracy and reliability compared to the state of the art.

在生物学中使用显微镜进行定量测量可以了解细胞功能。在低光子计数的情况下，这种测量的可靠性和灵敏度是必要的基础，因为为了避免光漂白或对细胞的损害（光毒性），需要低光子计数。在这个项目中，我们将实现这样一个强大的荧光显微镜测量系统。它将首次结合PSF估计、分割和反卷积的最佳方法。我们将开发一种使用泽尼克多项式的PSF估计方法，该方法适用于物理可行PSF的自由度和系统的物理噪声模型。我们通过使用组合分割盲反卷积方法扩展了该方法，将PSF估计限制在我们分割已知结构属性对象的区域。这种组合不仅会带来更好的结果，我们还将克服已知的盲反卷积的局限性，即对PSF核的不敏感。为了使系统应用于实际，采用了新的方法来加快计算速度，如使用管法、自适应恢复和有序子集。与目前的技术水平相比，我们期望在准确性和可靠性方面有显著的提高。