SABER: Scalable Analytics for Brain Exploration Research using X-Ray Microtomography and Electron Microscopy

SABRE：使用 X 射线显微断层扫描和电子显微镜进行大脑探索研究的可扩展分析

• 批准号: 9414126
• 负责人: William R Gray Roncal
• 金额: $ 39.55万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-21 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9414126
• 关键词: Acute; Address; Algorithms; Artificial Arm; Artificial Intelligence; Bioinformatics; Biological; Brain; Cell Count; Cell Density; Code; Collaborations; Communities; Computer software; Computers; Data; Data Discovery; Data Set; Development; Disease; Educational workshop; Electron Microscopy; Electrons; Ensure; Environment; Evaluation; Grant; Human Resources; Image; Image Analysis; Imagery; Imaging Techniques; Imaging technology; Individual; Intelligence; Knowledge; Knowledge Extraction; Laboratories; Magnetic Resonance Imaging; Maps; Measurement; Microscopic; Modality; Modernization; Mus; Neuroanatomy; Neurodegenerative Disorders; Neurons; Neurosciences; Optics; Physics; Pluto; Process; Protocols documentation; Reproducibility; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resolution; Retrieval; Roentgen Rays; Running; Science; Scientist; Source; Standardization; Structure; System; Techniques; Technology; Tissue imaging; Tissues; Training; Translating; Traumatic Brain Injury; Universities; Work; brain research; brain tissue; computer science; computerized data processing; connectome; data access; data archive; data management; density; design; experience; experimental study; high resolution imaging; improved; innovative neurotechnologies; microscopic imaging; millimeter; nervous system disorder; neuroimaging; neuron loss; novel; novel strategies; portability; relating to nervous system; scaffold; software development; terabyte; tool; tool development; user-friendly; virtual

Project Abstract Advances in imaging have had a profound effect on our ability to generate high-resolution measurements of the brain’s structure. One of the major hurdles in processing modern neuroimaging datasets designed to produce large-scale maps of the connections and the organization of the brain lies in the sheer size of these data. For instance, electron microscopic (EM) images of a cubic millimeter of cortex occupies roughly 3 PBon disk, and lower resolution emerging X-ray microtomography (XRM) data can exceed 10 TB for a single mouse brain. When dealing with datasets of this size, the application of even simple algorithms becomes difficult. The size of datasets also exacerbates the considerable challenges for dissemination, reproducibility, and collaboration across laboratories. Addressing these challenges requires a new approach that leverages state-of-the-art computer science technology while remaining conscientious of the underlying bioinformatics. We propose Scalable Analytics for Brain Exploration Research (SABER), a user-friendly and portable framework that automates the retrieval, extraction, and analysis of large-scale imagery data to facilitate neuroscientific analyses. SABER aims to improve the reliability and reproducibility of neuroimagery research by providing a common substrate upon which algorithms may be developed. Leveraging SABER’s containers — a standardized packaging for software — this substrate can then be trivially transferred to other machines by the same researcher or by other teams aiming to reproduce or adapt the prior work, making sharing workflows and extracting knowledge commonplace. Using SABER will ensure that the analysis runs identically, regardless of by whom or where the workflow is executed. Because developing and deploying these analysis solutions for large image volumes are acute barriers to developing consistently reproducible workflows, SABER will further the neuroscientific analysis community by simplifying the workflow-development and workflow-execution steps. To demonstrate this, we plan to distribute two community-vetted, optimized workflows to convert large-scale EM and XRM volumetric imagery into maps of neuronal connectivity. Many neurological diseases are characterized by their impact on the density of cells and vessels, neuron death, connectivity, or other factors that are visible with imaging technologies. SABER will provide a framework for producing reproducible estimates of cell counts, vasculature density, and connectomes, thus enabling increased understanding of the impact of disease on the neuroanatomy of many brains. This work will enable the development of tools that can both be applied to massive data and shared amongst many scientists, which will in turn accelerate progress and neuroscientific discovery.

项目摘要 成像技术的进步对我们生成高分辨率测量结果的能力产生了深远的影响 大脑的结构是处理现代神经影像数据集的主要障碍之一。 生成大比例尺的连接图，大脑的组织取决于这些图的绝对大小 例如，一立方毫米皮质的电子显微镜 (EM) 图像大约占据 3 PBon 磁盘，而较低分辨率的新兴 X 射线显微断层扫描 (XRM) 数据单个数据可能超过 10 TB。 当处理这种大小的数据集时，即使是简单的算法也变得难以应用。 数据集的规模也加剧了传播、再现性、 解决这些挑战需要一种利用新方法。 最先进的计算机科学技术，同时保持对基础生物信息学的认真态度。 我们提出了用于大脑探索研究的可扩展分析（SABRE），这是一种用户友好且便携式的 自动检索、提取和分析大规模图像数据的框架，以促进 SABRE 旨在提高神经影像研究的可靠性和可重复性。 通过提供可以利用 SABRE 容器开发算法的通用基础。 - 软件的标准化包装 - 然后可以将该基材轻松转移到其他机器 由同一研究人员或其他团队旨在复制或改编先前的工作，进行共享 使用 SABRE 将确保分析顺利进行。 无论工作流程由谁或在何处执行，都是相同的。 因为开发和部署这些针对大图像量的分析解决方案是严重的障碍 SABRE 开发一致的可重复工作流程，将进一步推动神经科学分析社区的发展 为了证明这一点，我们计划简化工作流程开发和工作流程执行步骤。 分发两个经过社区审查的优化工作流程来转换大规模 EM 和 XRM 体积 许多神经系统疾病的特点是对神经连接的影响。 细胞和血管的密度、神经元死亡、连接性或其他成像可见的因素 SABRE 将提供一个用于产生可重复的细胞计数估计的框架， 脉管系统密度和连接体，从而使人们能够更好地了解疾病对 这项工作将能够开发出可应用于许多大脑的神经解剖学工具。 海量数据并在许多科学家之间共享，这反过来将加速神经科学的进步 发现。