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

项目摘要 成像的进步已经对我们产生高分辨率测量的能力产生了深远的影响 大脑的结构。处理现代神经成像数据集的主要障碍之一是 制作连接的大型地图，大脑的组织取决于这些连接的大小 数据。例如，一立方毫米皮质的电子显微镜(EM)图像约占磁盘上的3PB，而较低分辨率的新兴X射线显微断层扫描(XRM)数据可以超过单个X射线显微断层扫描(XRM)数据10TB 老鼠的大脑。在处理这种大小的数据集时，即使是简单算法的应用也变得 很难。数据集的大小也加剧了传播、可重复性、 以及跨实验室协作。应对这些挑战需要一种新的方法来利用 最先进的计算机科学技术，同时对潜在的生物信息学保持认真的态度。 我们提出了Scalable Analytics for Brain Explore Research(SABER)，一个用户友好且可移植的工具 自动检索、提取和分析大规模图像数据的框架，以便于 神经科学分析。Saber旨在提高神经成像研究的可靠性和重复性 通过提供可在其上开发算法的公共衬底。利用军刀的容器 -软件的标准化包装-然后可以将该基板简单地转移到其他机器上 由同一研究人员或旨在复制或改编先前工作的其他团队进行分享 工作流程和知识提取司空见惯。使用Saber将确保分析运行 同样，无论由谁或在哪里执行工作流。 因为开发和部署这些针对大容量图像的分析解决方案是 开发一致可重复的工作流程，Saber将进一步推动神经科学分析社区 通过简化工作流开发和工作流执行步骤。为了证明这一点，我们计划 分发两个经过社区审查、优化的工作流，以转换大规模EM和XRM卷 将图像转换成神经元连接的地图。许多神经系统疾病的特点是它们对 细胞和血管的密度、神经元死亡、连通性或其他通过成像可见的因素 技术。Saber将提供用于产生细胞计数的可重现估计的框架， 血管密度和连接，从而增加了对疾病影响的了解 对许多大脑的神经解剖学。这项工作将使工具的开发既可以应用于 海量数据并在许多科学家之间共享，这反过来将加速进步和神经科学 发现号。

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.