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Hadwiger, Markus

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Hadwiger

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Markus

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Hadwiger, Markus
Author's Publications: search.filters.isAuthorOfPublication.74f8a123-2779-4778-86e6-cdbe9d19aeb6

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    Exploring the Connectome: Petascale Volume Visualization of Microscopy Data Streams
    (Institute of Electrical & Electronics Engineers (IEEE), 2013) Beyer, Johanna; Hadwiger, Markus; Al-Awami, Ali; Jeong, Won-Ki; Kasthuri, Narayanan; Lichtman, Jeff; Pfister, Hanspeter
    Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and dendrites as well as synapses have been segmented and labeled.
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    NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity
    (Institute of Electrical & Electronics Engineers (IEEE), 2014) Al-Awami, Ali; Beyer, Johanna; Strobelt, Hendrik; Kasthuri, Narayanan; Lichtman, Jeff; Pfister, Hanspeter; Hadwiger, Markus
    We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.
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    A Survey of GPU-Based Large-Scale Volume Visualization
    (IEEE Visualization and Graphics Technical Committee (IEEE VGTC), 2014) Beyer, Johanna; Hadwiger, Markus; Pfister, Hanspeter
    This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera-, and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e., “output-sensitive” algorithms and system designs. This leads to recent outputsensitive approaches that are “ray-guided,” “visualization-driven,” or “display-aware.” In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we discuss in this survey.
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    SSECRETT and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Datasets
    (2010) Jeong, Won-Ki; Beyer, Johanna; Hadwiger, Markus; Blue, Rusty; Law, Charles; Vazquez, Amelio; Reid, Clay; Lichtman, Jeff; Pfister, Hanspeter
    Recent advances in optical and electron microscopy allow scientists to acquire extremely high-resolution images for neuroscience research. Datasets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte in size. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible and interactive tools for their scientific work we introduce SSECRETT and NeuroTrace, two systems that were designed for interactive exploration and analysis of large-scale optical and electron microscope images to reconstruct complex neural circuits of the mammalian nervous system.