Venkat A, Christensen C, Gyulassy A, Summa B, Federer F, Angelucci A, Pascucci V
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
Department of Computer Science, Tulane University.
N Y Sci Data Summit NYSDS. 2016 Aug;2016. doi: 10.1109/NYSDS.2016.7747805. Epub 2016 Nov 21.
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.
最近兴起的连接组学领域的目标是生成不同尺度下大脑的布线图。为了识别大脑回路,神经科学家使用专门的显微镜以非常高的分辨率对标记神经元进行多通道成像。CLARITY组织透明化技术允许通过整个组织块对标记回路进行成像,而无需进行组织切片和切片间的对齐。特别是,要以足够的分辨率对大型且复杂的非人灵长类动物大脑进行成像,以识别轴突并消除其歧义,会产生海量数据,给神经回路研究带来巨大的计算挑战。研究人员需要用于编译、拼接和可视化大型图像的新型软件功能。在这项工作中,我们详细介绍了图像采集过程以及一个分层流平台ViSUS,该平台能够使用标准台式计算机对这些海量多体积数据集进行交互式可视化。ViSUS可视化框架此前已被证明适用于3D燃烧模拟、气候模拟以及大规模全景图像的可视化。该平台围绕一种称为IDX文件格式的分层缓存无关数据布局构建,这使得在ViSUS中能够进行交互式可视化和探索,并能扩展到最大的3D图像。在本文中,我们展示了在与显微镜数据的交互式设置中使用的VISUS框架。