Scaling Volumetric Imaging, Analysis and Science Communication Using Immersive Virtual Reality

在过去的15年里，用于高通量成像的新显微镜技术和方法 通过扩展数据集的分辨率和规模，彻底改变了结构生物学， 尺寸.生成的映像卷通常为数百GB甚至数十TB 而对于脑的大体积电子显微镜图像，可以接近PB大小。这些文件 尺寸对图像分析和代表性的一组原始图像的通信提出了挑战。 数据和量化。大型文件包含许多结构，需要机器学习（ML） 在一个允许纠错的环境中的策略。科学传播需要工具， 准备好获取原始数据，以及更有效的方法来传达快速积累的 科学信息的集合。快速积累的数字图书馆也为以下方面提供了资源： 教育和培训，这在很大程度上是未开发的。 我们建议利用虚拟现实（VR）来改变这些挑战， 对立体视觉和模式识别的自然能力， 沟通，教学和培训，听觉处理语言和本地化 声音.基于我们现有的工具库和大文件的直接体绘制 在我们的虚拟现实软件syGlass中，我们将首先扩展现代领域学习， ML领域中所谓的元学习技术，只需很少的迭代即可分析图像 从目标计数到目标跟踪（目标1）。接下来，我们将利用新的 云渲染技术，以显著降低采用 syGlass（Aim 2）.最后，我们将提供新的工具，以有效地产生叙述科学 VR演示文稿用于实验室设置，作为手稿出版物，并用于生产 教育材料（目标3）。大脑的复杂性提供了一个具有挑战性的测试平台， 教学和培训。在每一个目标中，我们将在分析中引入范式转变， 大数据量，以及与同事和非专家的3D和4D数据通信。

Over the past 15 years, new microscope technologies and methods for high throughput imaging have revolutionized structural biology by extending the resolution and scale of datasets in 3 dimensions. The resulting image volumes are more typically hundreds of GB to even tens of TB and for large volume electron microscope images of brain, can approach PB sizes. These file sizes pose challenges for image analysis, and communication of a representative set of raw data and quantification. Large files contain many structures, and require machine learning (ML) strategies in a context that permits error correction. Scientific communication requires tools for ready access to raw data, and more efficient methods to communicate the rapidly accumulating sets of scientific information. The rapidly accumulating digital library also affords a resource for teaching and training, which is largely untapped. We propose to leverage virtual reality (VR) to transform each of these challenges, capitalizing on natural abilities for stereoscopic vision and pattern recognition and, for scientific communication, teaching and training, auditory processing to process language and localize sounds. Based upon the tool base and direct volume rendering of large files that we have established in our VR software, called syGlass, we will first expand modern domain learning and so-called meta-learning techniques in the ML field to analyze images with few iterations from object counting to object tracking and tracing (Aim 1). Next, we will capitalize on new technologies for cloud rendering to significantly mitigate the hardware costs for adoption of syGlass (Aim 2). Finally, we will provide novel tools to efficiently generate narrated scientific presentations in VR for use in the lab setting, as manuscript publications, and for production of educational materials (Aim 3). The complexity of the brain offers a challenging testbed for teaching and training. In each of these Aims, we will introduce paradigm shifts in the analysis of the large data volumes, and communication of 3D and 4D data to colleagues and non-experts.