Li Yuxin, Li Anan, Li Junhuai, Zhou Hongfang, Cao Ting, Wang Huaijun, Wang Kan
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
Shaanxi Key Laboratory of Network Computing and Security Technology, Xi'an, China.
Front Neuroinform. 2021 Jan 13;14:542169. doi: 10.3389/fninf.2020.542169. eCollection 2020.
The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.
介观全脑成像技术的普及程度急剧提高，但这些技术会生成万亿体素大小的体积图像数据。在生物图像分析流程中，可视化这些海量数据或与之交互既必要又至关重要；然而，由于数据量巨大，研究人员难以使用普通计算机对其进行处理。现有的解决方案没有考虑同时应用网络可视化和三维（3D）体绘制方法，以减少数据复制操作的次数，并提供一种更好的方式来可视化生物图像数据中的3D结构。在此，我们提出了webTDat，一个用于介观尺度全脑成像数据集的基于网络的开源实时3D可视化框架。webTDat采用了一种先进的渲染可视化方法，该方法采用了创新的数据存储格式和并行渲染算法。webTDat首先加载图像中的主要信息，然后决定是否需要加载图像中的次要信息。通过对TB级全脑数据集进行验证，webTDat在网络可视化过程中实现了实时性能。webTDat框架还提供了丰富的注释接口，使其成为可视化介观全脑成像数据的有用工具。
