A web-based framework for multi-modal visualization and annotation of neuroanatomical data

基于网络的神经解剖数据多模式可视化和注释框架

• 批准号: 10365435
• 负责人: David Kleinfeld
• 金额: $ 163.45万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-16 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10365435
• 关键词: 3-Dimensional; Anatomy; Animal Experiments; Animal Model; Animals; Atlases; Attention; BRAIN initiative; Beds; Big Data; Brain; Brain Diseases; Cell physiology; Cells; Cerebrovascular system; Collaborations; Communities; Complex; Computer software; Cytology; Data; Data Display; Data Set; Databases; Development; Distant; Distributed Databases; Documentation; Electrodes; Electron Microscopy; Electrophysiology (science); Ensure; Evaluation; FOS gene; Feedback; Foundations; Functional Imaging; Genetic Markers; Goals; Guidelines; Histology; Hour; Image; Image Analysis; Implant; Instruction; Investments; Label; Laboratories; Lateral; Lead; Learning; Light; Machine Learning; Metadata; Methods; Microscopy; Midbrain structure; Modernization; Modification; Molecular; Names; Neurons; Neurosciences; Online Systems; Pathway interactions; Physiological; Research; Research Personnel; Resolution; Resources; Sampling; Scanning; Scientist; Site; Slice; Software Tools; Specificity; Stains; Standardization; Stress Tests; Surveys; Techniques; Testing; Time; Tracer; Training; United States National Institutes of Health; Viral; Visualization; annotation system; automated segmentation; base; cloud based; cognitive system; computing resources; density; imaging modality; interest; interoperability; laptop; large datasets; light microscopy; multimodality; neurovascular; performance site; programs; reconstruction; relating to nervous system; skills; software development; terabyte; three-dimensional visualization; tool; two-photon; user-friendly; web site; web-based tool

PROJECT SUMMARY/ABSTRACT Modern experimental approaches allow researchers to collect a variety of whole-brain data from the same animal via different anatomical labels, including tracers, genetic markers, and fiducial marks from recording electrodes. Unfortunately, viewing and analysis methods have not kept pace with the complexity of these datasets, which can be as large as several terabytes. This limitation makes it time- and resource-intensive to view and manipulate light-microscopy data or to share these datasets with distant laboratories. Currently available software solves some aspects of this problem, but no existing program provides a user-friendly way to visualize, annotate, and compare large neuroanatomical datasets across research sites, with minimal investment of computational resources. We propose to develop a web-based tool, named BrainSharer, to allow researchers to access, visualize, align, share, and semi-automatically annotate brain-wide data within a common framework. The foundation for this tool will be provided by Neuroglancer, a generic web-based volumetric viewer first developed at Google and then adapted for use in electron microscopy laboratories. While some of its current features are useful across applications, existing versions of Neuroglancer are not optimized for light-microscopy data. In particular, they do not realize the potential for sharing, viewing, and editing data across multi-laboratory collaborations, such as U19 projects. To enable BrainSharer to serve data rapidly and to save and restore sessions, we will add a modular distributed database to synchronize metadata across laboratories. In addition, we will tailor BrainSharer for light microscopy by displaying data in formats independent of the imaging modality, adding semiautomatic means to segment cell bodies and processes, adding tools for annotation (with special attention to defining cytological boundaries in three dimensions and tracing projection pathways), and adding ways to incorporate auxiliary data such as electrode tracks. In addition, we will integrate alignment tools into BrainSharer, so that separate datasets can be co-registered, visualized, and annotated in the same framework, along with established and emerging atlases. As test beds for development of BrainSharer, we will use three types of datasets from our U19 projects: whole-brain disynaptic and polysynaptic tracing, activity-based staining with c-fos, and neurovascular data. All software, training datasets, and video tutorials for BrainSharer will be made freely available to the community, hosted on our website, along with a slice histology dataset and an electrophysiology dataset with probes implanted throughout the brain. To orient new users, we will also provide a Jupyter notebook for converting raw, intermediate, and registered light-sheet data, along with detected cells and brain atlases, to precomputed format, so they can be loaded into BrainSharer. When complete, BrainSharer will make it straightforward for researchers to use their laptops to combine and compare large datasets from different anatomical labels for viewing and analysis relative to reference atlases, and to share this information across performance sites, thus increasing the ease of use and interoperability of big data in neuroscience.

项目总结/摘要 现代实验方法允许研究人员从同一动物身上收集各种全脑数据 通过不同的解剖标签，包括示踪剂，遗传标记，和来自记录电极的基准标记。 不幸的是，查看和分析方法没有跟上这些数据集的复杂性， 可以大到几个TB。这种限制使得查看和操作 光学显微镜数据或与远程实验室共享这些数据集。目前可用的软件解决了 这个问题的一些方面，但没有现有的程序提供了一个用户友好的方式来可视化，注释， 比较研究中心之间的大型神经解剖数据集， 资源我们建议开发一个基于网络的工具，名为BrainSharer，允许研究人员访问， 可视化、对齐、共享和半自动注释通用框架内的全脑数据。的 Neuroglancer是一种基于网络的通用体积查看器，它首先开发了一种基于网络的体积查看器 在谷歌，然后适用于电子显微镜实验室。虽然它目前的一些功能是 尽管Neuroglancer在应用程序中非常有用，但现有版本的Neuroglancer并未针对光学显微镜数据进行优化。在 特别是，他们没有意识到跨多个实验室共享、查看和编辑数据的潜力 例如U19项目。使BrainSharer能够快速提供数据并保存和恢复 会议，我们将增加一个模块化的分布式数据库，以同步跨实验室的元数据。此外，本发明还提供了一种方法， 我们将为光学显微镜定制BrainSharer，以独立于成像模式的格式显示数据， 添加半自动方法来分割细胞体和过程，添加注释工具（具有特殊的 注意在三维空间中定义细胞学边界并追踪投影路径），并添加 结合诸如电极轨迹的辅助数据的方法。此外，我们还将整合对齐工具， BrainSharer，这样单独的数据集可以在同一个框架中共同注册，可视化和注释， 沿着而来的还有已建立和新兴的地图集。作为BrainSharer开发的测试平台，我们将使用三个 我们U19项目的数据集类型：全脑双突触和多突触追踪，基于活动的染色 c-fos和神经血管数据。BrainSharer的所有软件、训练数据集和视频教程都将 免费提供给社区，托管在我们的网站上，沿着切片组织学数据集和 电生理学数据集与探针植入整个大脑。为了引导新用户，我们还将 一种用于转换原始、中间和记录的光片数据以及沿着检测到的细胞的字节笔记本 和大脑地图集，以预先计算的格式，这样他们就可以加载到BrainSharer。完成后，BrainSharer 将使研究人员能够直接使用笔记本电脑来联合收割机和比较来自 不同的解剖标签，用于查看和分析相对于参考图集，并共享此信息 跨性能站点，从而提高神经科学中大数据的易用性和互操作性。