Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations. Visualisation is often the first step in making sense of the data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR AR visualisation framework for the Java VM that can handle mesh and arbitrarily large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and detail example applications, such as its use in the biomedical image analysis software Fiji, or for visualising agent-based simulations.
In the field of biomedical image analysis, various exist: , , and See the corresponding websites at https: www.arivis.com en imaging-science imaging-science arivis.com en imaging-science imaging-science , https: www.fei.com software amira fei.com software amira , https: imaris.oxinst.com imaris.oxinst.com , and @cite_5 support out-of-core rendering, and are scriptable by the user. Arivis, Imaris, and also syGlass offer rendering to VR headsets, and Amira can run on CAVE systems. Imaris provides limited Fiji and Matlab integration. Due to being closed-source, the flexibility of these software packages is ultimately limited (, changing rendering methods, or adding new input devices).
@cite_5
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The quest for deeper understanding of biological systems has driven the acquisition of increasingly larger multidimensional image datasets. Inspecting and manipulating data of this complexity is very challenging in traditional visualization systems. We developed syGlass, a software package capable of visualizing large scale volumetric data with inexpensive virtual reality head-mounted display technology. This allows leveraging stereoscopic vision to significantly improve perception of complex 3D structures, and provides immersive interaction with data directly in 3D. We accomplished this by developing highly optimized data flow and volume rendering pipelines, tested on datasets up to 16TB in size, as well as tools available in a virtual reality GUI to support advanced data exploration, annotation, and cataloguing.