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.

该项目将创建自动化系统，用于分析大型多模式生物医学数据以增强 肯特州立大学及其他地区的教育和研究基础设施。完成的任务将 提供支持并提高至少50个研究实验室分析数据（准确性和速度）的能力， 以及通过整合到研究计划和课程中的200多名学生培训超过200名学生。我们将1） 创建算法以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）创建自动化动态磁共振处理的方法 成像（MRI）数据，3）可以在动物模型中评估新方法，以及4） 生成与疾病细胞种群变化有关的数据，以提供新的治疗途径。作为 我们实现了这些目标，我们将在至少三个领域中支持和加强教育； 1）增强 三个实验室中的生物医学成像研究技术培训，2） 纪律课程基于资源的开发，包括“应用生物医学数据 处理”和“生物图像分析”，以及3）开发基础架构以持续使用和 最终用户开发基于群集的并行数据处理系统， 全球研究人员。激光扫描和三维电子显微镜产生数据 由数千个构成大量数据的顺序图像组成。功能和结构 MRI系统通常用于使用多种方式来扫描受试者和患者 （fMRI，扩散加权，T1/T2）。这些类型的阵列可以具有数千个图像和/或离散 每种方式产生复杂数据的时间点，需要大量的人类时间（天）来处理 经常需要子采样。我们的长期目标是支持所有类型的自动数据分析 与人类健康有关，因此该项目的主要重点是创建一个可扩展的平台和 完全支持所有计算昂贵的数据分析的方法。我们最初将把精力集中在 创建用于从大型显微镜数据（大量）自动提取和分析神经胶质细胞的工具 空间组织图），动态MRI数据的自动分割和分析并自动化大数据 使用并行系统处理。这些方法将用于评估细胞群体的变化 发生在疾病的动物模型中，以识别新的策略和治疗方法。这 将大大增强肯特州立大学的研究和教育基础设施，并包括 开发新方法来自动化生物医学数据分析以及资源 许多研究小组的自动应用以及继续使用这些常规和现有例程。更远， 通过创建新课程，以及在潜水员实验室中的多学科研究活动的整合， 数百名学生将接受方法和技术的应用和开发培训。