Abstract
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.
Highlights
The brain is one of the most complex organs in nature
The results showed that this format achieves an excellent performance with the described fine-grained data division, and it proved suitable for network transmission and visualization
This study proposed webTDat, a web-based, real-time 3D interactive visualization framework for large-scale volume images
Summary
The brain is one of the most complex organs in nature. Due to the large number of neurons in the brain and the complex structural connections between them, mapping a brain-wide network at mesoscopic resolution is critical for revealing brain function mechanisms (Lichtman and Denk, 2011; Koch and Reid, 2012). With the development of transgenic technology, neuronal tracers, and optical imaging, mesoscopic-scale whole-brain imaging has become a vital imaging technology for obtaining high-resolution brain connectivity information throughout the entire brain (Osten and Margrie, 2013; Mitra, 2014) These technologies include micro-optical sectioning tomography (MOST) (Li et al, 2010; Gong et al, 2016), serial two-photon tomography (STP) (Ragan et al, 2012; Economon et al, 2016), and light-sheet microscopy (LSM) (Niedworok et al, 2012), which can rapidly scan a complete mouse brain at submicrometric resolution in three dimensions or webTDat multiple dimensions. The large size of mesoscopic whole-brain data has posed a significant challenge to efficient visualization of and interaction with these datasets (Helmstaedter and Mitra, 2012), which are basic and essential tasks in the bioimage analysis pipeline (Peng, 2008; Walter et al, 2010; Meijering et al, 2016)
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