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A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data

机译:可扩展的网络基础设施用于万亿级显微镜数据的交互式可视化

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摘要

The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data.
机译:最近出现的连接组学领域的目标是生成不同比例的大脑接线图。为了识别大脑电路,神经科学家使用专门的显微镜以非常高的分辨率对标记的神经元进行多通道成像。清晰的组织清除功能可让整个组织块中的标记电路成像,而无需进行组织切片和逐段对齐。用足够的分辨率对大型复杂的非人类灵长类动物大脑进行成像,尤其是可以识别和消除轴突之间的歧义,尤其是产生大量数据,这对神经回路的研究提出了巨大的计算挑战。研究人员需要新颖的软件功能来编译,拼接和可视化大图像。在这项工作中,我们详细介绍了图像采集过程和分层流平台ViSUS,该平台可使用标准台式计算机对这些庞大的多体积数据集进行交互式可视化。先前已证明ViSUS可视化框架适用于3D燃烧模拟,气候模拟和大规模全景图像的可视化。该平台是围绕分层缓存遗忘数据布局(称为IDX文件格式)组织的,该布局可在ViSUS中进行交互式可视化和浏览,并扩展到最大的3D图像。在本文中,我们展示了用于与显微镜数据进行交互设置的VISUS框架。

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