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.

The goal of this project is to scale analytics for large image volumes and thereby accelerate discovery of new biological principles, and remove barriers to communicate and receive this information. We propose to leverage virtual reality to change the way users interact with their data, by providing cutting-edge machine learning methods interpolated with rapid error correction. We propose also that virtual reality can underlie a new mode of interaction among scientific colleagues around 3D/4D data sets, and permit them to make the full extent of their discoveries accessible to experts and non- experts.