Automating Biomedical Data Analysis

自动化生物医学数据分析

• 批准号: 10047049
• 负责人: Robert J Clements
• 金额: $ 44.61万
• 依托单位: KENT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10047049
• 关键词: 3-Dimensional; Algorithms; Animal Disease Models; Animal Model; Architecture; Area; Astrocytes; Big Data; Biological; Biological Assay; Biomedical Research; Cells; Cellular Morphology; Communities; Computer software; Critical Thinking; Cuprizone; Data; Data Analyses; Data Set; Demyelinations; Dependence; Detection; Development; Diagnosis; Disease; Education; Educational Curriculum; Electron Microscopy; Environment; Functional Magnetic Resonance Imaging; Funding; Generations; Goals; Health; Human; Image; Image Analysis; Infrastructure; Interdisciplinary Study; Lasers; Magnetic Resonance; Magnetic Resonance Imaging; Manuals; Maps; Medical Imaging; Methods; Microscopic; Microscopy; Modality; Modeling; Morphology; Mus; Neuroglia; Occupations; Outcome; Patients; Performance; Phase; Phenotype; Play; Population; Process; Proteins; Research; Research Activity; Research Infrastructure; Research Personnel; Research Technics; Resolution; Resource Development; Resources; Role; Route; Sampling; Scanning; Schedule; Series; Slave; Speed; Statistical Methods; Structure; Students; System; Systems Analysis; Techniques; Therapeutic Intervention; Time; Tissue Stains; Tissues; Training; Universities; algorithmic methodologies; automated analysis; automated segmentation; base; bioimaging; biomedical scientist; career; career networking; cluster computing; college; complex data; computerized data processing; diffusion weighted; discrete time; disorder prevention; graduate student; imaging system; improved; instrumentation; laboratory curriculum; method development; multidisciplinary; multimodal data; multimodality; novel therapeutics; open source; programs; response; skills; student training; technique development; tool

This project will create automated systems for analyzing big multimodal biomedical data to enhance the educational and research infrastructure at Kent State University and beyond. The completed tasks will provide support and improve the ability to analyze data (accuracy and speed) for at least 50 research labs, as well as train well over 200 students via integration into research programs and the curriculum. We will 1) Create algorithms to automate processing of large spatial biomedical data to automatically extract and analyze thousands of cells, 2) Create methods to automate the processing of dynamic magnetic resonance imaging (MRI) data, 3) Empirically evaluate the new methods in an animal model of disease, and,4) Generate data relating to changes in cell populations in disease to provide new therapeutic avenues. As we accomplish these goals we will support and strengthen education in at least three areas; 1) Enhance student training in biomedical imaging research techniques in three labs, 2) Create recurring multi- disciplinary courses based around development of the resources including “Applied Biomedical Data Processing” and “Biological Image Analysis” , and, 3) Develop the infrastructure for continued use and development for end users with a cluster-based parallelized data processing system for students and researchers worldwide. Laser scanning and three-dimensional electron microscopy produce data consisting of thousands of sequential images making up large volumes of data. Functional and structural MRI systems are routinely used to scan subjects and patients over many months using multiple modalities (fMRI, diffusion weighted, T1/T2). These types of arrays can have thousands of images and/or discrete time-points per modality generating complex data requiring significant human time (days) to process where sub-sampling is frequently required. Our long term goal is to support all types of automated data analysis pertinent to human health, and as such a major focus of this project is to create an extensible platform and methods to fully support all computationally expensive data analysis. We will initially focus our efforts on creating tools for the automated extraction and analysis of glial cells from large microscopy data (massive spatial tissue maps), automate segmentation and analysis of dynamic MRI data and automate big data processing using parallel systems. The methods will be used to evaluate changes to cell populations occurring in an animal model of disease to identify new strategies and manipulations for treatments. This will significantly enhance the research and educational infrastructure at Kent State University and include the development of new methods to automate biomedical data analysis as well as resources for the automated application and continued use of these and existing routines by many research groups. Further, by the creation of new courses, and integration of the multidisciplinary research activities in diverse labs, hundreds of students will be trained in the application and development of the methods and techniques.

该项目将创建用于分析大型多模式生物医学数据的自动化系统，以增强 肯特州立大学及其他地区的教育和研究基础设施已完成的任务将。 为至少 50 个研究实验室提供支持并提高分析数据的能力（准确性和速度）， 以及通过融入研究项目和课程来培训 200 多名学生，我们将 1)。 创建算法来自动处理大型空间生物医学数据，以自动提取和 分析数千个细胞，2) 创建自动处理动态磁共振的方法 成像 (MRI) 数据，3) 根据经验评估疾病动物模型中的新方法，以及，4) 生成与疾病中细胞群变化相关的数据，以提供新的治疗途径。 为了实现这些目标，我们将至少在三个领域支持和加强教育：1）加强； 在三个实验室进行生物医学成像研究技术的学生培训，2）创建重复的多 基于资源开发的学科课程，包括“应用生物医学数据” 3) 开发持续使用的基础设施 为最终用户开发基于集群的并行数据处理系统，供学生和 世界各地的研究人员通过激光扫描和三维电子显微镜生成数据。 由数千个连续图像组成，构成大量功能和结构数据。 MRI 系统通常使用多种方式对受试者和患者进行数月的扫描 （fMRI、扩散加权、T1/T2）。这些类型的阵列可以有数千个图像和/或离散图像。 每种模式的时间点生成复杂的数据，需要大量的人力时间（天）来处理 我们的长期目标是支持所有类型的自动化数据分析。 与人类健康相关，因此该项目的主要重点是创建一个可扩展的平台 完全支持所有计算成本较高的数据分析的方法，我们最初将集中精力进行研究。 创建从大型显微镜数据（大量数据）中自动提取和分析神经胶质细胞的工具 空间组织图），自动分割和分析动态 MRI 数据并自动化大数据 这些方法将用于评估细胞群的变化。 发生在疾病动物模型中，以确定新的治疗策略和操作。 将加强肯特州立大学的研究和教育基础设施，包括 开发自动化生物医学数据分析的新方法以及资源 许多研究小组自动应用并继续使用这些和现有的例程。 通过创建新课程以及整合不同实验室的多学科研究活动， 数百名学生将接受有关方法和技术的应用和开发的培训。