Image Enhancement and Segmentation for Deep Tissue Microscopy

深层组织显微镜图像增强和分割

• 批准号: 7894788
• 负责人: Paul Salama
• 金额: $ 19.04万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-16 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7894788
• 关键词: Address; Adopted; Area; Biomedical Research; Characteristics; Complex; Computer software; Data; Databases; Development; Elements; Goals; Image; Image Analysis; Image Enhancement; Individual; Lead; Link; Methods; Microscopy; Modeling; Morphology; Noise; Photons; Probability; Process; Research; Research Personnel; Resolution; Sampling; Shapes; Software Tools; Solutions; Structure; Techniques; Thick; Tissues; base; data modeling; fluorescence imaging; image processing; image reconstruction; imaging Segmentation; innovation; method development; multi-photon; object shape; platform-independent; public health relevance; reconstruction; restoration; tool

DESCRIPTION (provided by applicant): Current 2D and 3D deconvolution and analysis techniques are not well suited for deep tissue microscopy. Most assume the point spread functions (PSFs) to be symmetric or spatially invariant, which is not the case for thick samples. In addition, many utilize prior probability distributions that are not good models of the data that bias the enhancement results. Finally, many techniques are computationally intensive. The long term goal is to develop automatic and unbiased 3D enhancement (deconvolution), segmentation, and quantification tools suitable for deep-tissue multi-photon microscopy, and to implement these tools into multiplatform image analysis software for efficient, interactive quantitation of multi-photon image data. The objective of the current application is to develop efficient 2D enhancement and segmentation techniques that can be used in the analysis of multi-photon data. The approach will be based on (1) estimating the unknown probability distribution of the true but unknown images (i.e. the prior distribution) and (2) using the estimated prior distribution to enhance images prior to segmentation, rather than segmenting them directly. The estimate of the unknown probability distribution will be derived from the acquired data by assuming a Poisson model for the noise and by imposing some physical constraints that are characteristic of deep tissue imaging, such as low photon count. The rationale for the proposed research is that inaccurate assumptions about PSFs and the prior distributions lessen the quality of enhancement and consequently decrease the accuracy and sensitivity of segmentation techniques. The objective will be achieved through the following specific aims: (1) Estimating the prior distributions and using the estimated priors to enhance images via maximum a posteriori (MAP) estimation. The enhanced images will then be segmented via techniques that track object boundaries. (2) Utilizing mathematical morphology to segment objects of known shape from the enhanced images. This approach is innovative because it derives the unknown data probability distributions directly from the data by imposing constraints that are better suited for deep tissue imaging. This will result in the development of unique image analysis tools better suited to the unusual characteristics of deep-tissue fluorescence images. The proposed research is significant, because it will significantly enhance the quantitative capabilities of multi-photon microscopy. Public Health Relevance Statement: The proposed studies will address an under investigated area of deep tissue microscopy. The proposed research will enhance the ability to quantitatively analyze large microscopy image volumes, providing the final link in the effective implementation of multi-photon microscopy as a quantitative method in biomedical research.

描述（由申请人提供）：目前的2D和3D去卷积和分析技术不太适合深层组织显微镜检查。大多数假设点扩散函数（PSF）是对称的或空间不变的，这不是厚样本的情况。此外，许多利用先验概率分布，这不是数据的良好模型，这会使增强结果产生偏差。最后，许多技术是计算密集型的。长期目标是开发适用于深层组织多光子显微镜的自动且无偏见的3D增强（去卷积）、分割和量化工具，并将这些工具实施到多平台图像分析软件中，以高效、交互式地量化多光子图像数据。本申请的目的是开发有效的2D增强和分割技术，可用于多光子数据的分析。该方法将基于（1）估计真实但未知图像的未知概率分布（即先验分布）以及（2）使用估计的先验分布在分割之前增强图像，而不是直接分割它们。通过假设噪声的泊松模型并通过施加作为深部组织成像特征的一些物理约束（例如低光子计数），将从所采集的数据导出未知概率分布的估计。所提出的研究的理由是，不准确的假设PSF和先验分布降低增强的质量，从而降低分割技术的准确性和灵敏度。该目标将通过以下具体目标来实现：（1）估计先验分布，并使用估计的先验通过最大后验（MAP）估计来增强图像。然后，通过跟踪对象边界的技术对增强的图像进行分割。(2)利用数学形态学从增强图像中分割出已知形状的目标。这种方法是创新的，因为它通过施加更适合于深部组织成像的约束直接从数据导出未知数据概率分布。这将导致独特的图像分析工具的发展，更适合于不寻常的特点，深层组织荧光图像。这项研究意义重大，因为它将显著提高多光子显微镜的定量能力。 公共卫生相关性声明：拟议的研究将解决深部组织显微镜检查的研究领域。拟议的研究将提高定量分析大型显微镜图像体积的能力，提供多光子显微镜作为生物医学研究中的定量方法的有效实施的最终环节。