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 (OOCR), the rendering of volumetric data that does not fit into main or graphics memory, existing software packages include @cite_29 @cite_20 , which is written with applications like neuron tracing in mind, and @cite_3 , which performs by-slice rendering of arbitrarily large datasets, powered by the ImgLib2 library @cite_22 . Another application supporting OOCR is the @cite_4 , which lacks support for multiple timepoints and is not customizable. @cite_14 supports OOCR and interactive development, but does not support the overlay of multiple volumetric datasets in a single view.
@cite_14 @cite_4 @cite_22 @cite_29 @cite_3 @cite_20
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The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities. Tracing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists. Summary: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins. Availability: ImgLib2 is licensed under BSD. Documentation and source code are available at http: imglib2.net and in a public repository at https: github.com imagej imglib. Supplementary Information: Supplementary data are available at Bioinformatics Online. Contact: ed.gbc-ipm@dleflaas TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images