Continuous development of nTracer2 and its deployment at NIH image repositories

摘要 为了响应BRAIN Initiative的“集成和分析BRAIN Initiative数据”FOA，我们建议 进一步开发nTracer2，这是一个基于云的大脑图像可视化和神经元跟踪平台， Web浏览器。为了评估nTracer2的实际性能，我们在NIH部署了nTracer2 匹兹堡超级计算机中心的脑图像库（BIL）处理整个小鼠脑fMOST 荧光图像数据集。在一次公共数据访问测试中，多达50个用户顺利跨越地球仪 同时探索了这个数据集。在这个项目中，我们的团队将致力于四个目标。在目标1中，我们 建立我们新颖的SISF数据框架作为可扩展的图像数据标准，其中包括创建 更好的数据格式转换工具、压缩过滤器和SISF文档。在目标2中，我们 继续开发nTracer2（内容交付网络）CDN服务器，其中包括升级 数据管理接口和优化重建数据库的性能。在目标3中， 我们将继续开发nTracer2应用程序服务器，其中包括开发用户 管理工具，优化现有大脑配准的高性能计算性能 和机器视觉功能，整合由多模态显微镜模态拍摄的成像数据集， 并扩展其神经信息学功能。在目标4中，我们将在BIL部署全栈nTracer2， 集成CCF注册多模态数据集，存储在多个NIH存储库中。

Abstract In response to the BRAIN Initiative “Integration and Analysis of BRAIN Initiative Data” FOA, we propose further developing nTracer2, a cloud-based platform for brain image visualization and neuron tracing in the web browser. To evaluate nTracer2’s real-world performance, we deployed nTracer2 at the NIH Brain Image Library (BIL) at the Pittsburgh Supercomputer Center to handle whole mouse brain fMOST fluorescence image datasets. In a public data access test, up to 50 users across the globe smoothly explored this dataset simultaneously. In this project, our team will work on four Aims. In Aim 1, we will establish our novel SISF data framework as a scalable image data standard, which includes creating better data format conversion tools, compression filters, and documentation for SISF. In Aim 2, we will continue to develop the nTracer2 (content delivery network) CDN server, which includes upgrading the data management interface and optimizing the performance of the reconstruction database. In Aim 3, we will continue to develop the nTracer2 application server, which includes developing user management tools, optimizing high-performance computing performance for existing brain registration and machine vision functions, integrating imaging datasets taken by multimodal microscopy modalities, and expanding its neuroinformatics functions. In Aim 4, we will deploy full-stack nTracer2 at BIL and integrate CCF-registered multimodal datasets stored at multiple NIH repositories.