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Collaborative Research: Efficient mathematical and computational framework for biological 3D image data retrieval

协作研究：生物 3D 图像数据检索的高效数学和计算框架

基本信息

批准号：
1614661
负责人：
Atilla Sit
金额：
$ 14.34万
依托单位：
Eastern Kentucky University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1614661&HistoricalAwards=false
关键词：
Collaborative Research Efficient mathematical computational

项目摘要

Advances in imaging technology have led to a proliferation of three dimensional biological and medical image data from many imaging modalities, which include magnetic resonance imaging and computed tomography scans in medical imaging, neuroimaging using light-field microscopy in neuroscience, tomography for imaging cells and tissues, and cryo-electron microscopy for biomolecular structures. Images of three dimensional, volumetric, structures provide indispensable spatial information about organs, tissues, and molecules that cannot be captured using two dimensions. The development of tools for efficient and effective analysis of such volumetric data sets is, therefore, urgently required. This project will develop generally applicable mathematical and computational frameworks to effectively and accurately represent, compare, and retrieve biological and medical data in three dimensions. The methods to be developed will provide a general foundation for the analysis of volumetric images obtained using multiple imaging modalities and for multiple data types, not only from the biological domain. For example, the techniques have broader impact in areas such as human face recognition, analysis of geographical and climate data, and computer-aided design. This project, therefore, contributes to general promotion of the progress of science and technology in many domains in which imaging analysis is crucial and is of significant societal impact. In this project, two complementary and synergistic methods will be developed and integrated. The first method to be developed is a mathematical moment-based approach that provides a compact representation of volumetric data and is very suitable for localized three dimensional image data comparison. A two dimensional image comparison method that is based on a moment-based invariant will be expanded to handle volumetric data. The second method is a machine learning approach that will be powerful in classifying volumetric data. These two approaches will be integrated to take advantage of both methods and validated using three dimensional protein structural data. Analyzing global and local similarities between protein shapes is critical for understanding protein function but challenging because proteins with substantially different shapes may perform the same function. Further, proteins are appropriate for this validation step not only because many structures are available in well-established public databases but also because they lack intrinsic orientation, unlike previously studied datasets of man-made objects such as cars, cups, and tables. As the proposed methods are defined for a general voxel representation of a given volume, they will be generally applicable for any data set yielding a voxel representation, including biomedical data collected using electron microscopy, magnetic resonance imaging and computed tomography. Along side the scientific impact of the project, it also leverages efforts in the interdisciplinary computational life sciences and engineering departments at Purdue University and Eastern Kentucky University by recruiting and training students through interdisciplinary coursework and direct involvement with the project.

成像技术的进步导致来自许多成像方式的三维生物和医学图像数据激增，其中包括医学成像中的磁共振成像和计算机断层扫描、神经科学中使用光场显微镜的神经成像、用于成像细胞和组织的断层扫描以及用于生物分子结构的冷冻电子显微镜。三维、体积、结构的图像提供了有关器官、组织和分子的不可或缺的空间信息，而这些信息无法使用二维来捕获。因此，迫切需要开发对此类体积数据集进行高效且有效分析的工具。该项目将开发普遍适用的数学和计算框架，以有效、准确地表示、比较和检索三个维度的生物和医学数据。待开发的方法将为分析使用多种成像模式和多种数据类型获得的体积图像提供一般基础，而不仅仅是来自生物领域。例如，这些技术在人脸识别、地理和气候数据分析以及计算机辅助设计等领域具有更广泛的影响。因此，该项目有助于全面促进成像分析至关重要且具有重大社会影响的许多领域的科学技术进步。在该项目中，将开发和整合两种互补和协同的方法。第一种要开发的方法是基于数学矩的方法，它提供体积数据的紧凑表示，非常适合局部三维图像数据比较。基于矩不变量的二维图像比较方法将扩展到处理体积数据。第二种方法是机器学习方法，它在体积数据分类方面非常强大。这两种方法将被整合以利用这两种方法并使用三维蛋白质结构数据进行验证。分析蛋白质形状之间的全局和局部相似性对于理解蛋白质功能至关重要，但也具有挑战性，因为形状截然不同的蛋白质可能执行相同的功能。此外，蛋白质适合此验证步骤，不仅因为许多结构在完善的公共数据库中可用，而且因为它们缺乏内在方向，与之前研究的人造物体（例如汽车、杯子和桌子）的数据集不同。由于所提出的方法是针对给定体积的一般体素表示而定义的，因此它们通常适用于产生体素表示的任何数据集，包括使用电子显微镜、磁共振成像和计算机断层扫描收集的生物医学数据。除了该项目的科学影响之外，它还利用普渡大学和东肯塔基大学跨学科计算生命科学和工程系的努力，通过跨学科课程和直接参与该项目来招募和培训学生。