VCBM 14: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/7762

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Browsing VCBM 14: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Biology and genetics"

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    Interactive Labeling of Toponome Data
    (The Eurographics Association, 2014) Oeltze-Jafra, Steffen; Pieper, Franz; Hillert, Reyk; Preim, Bernhard; Schubert, Walter; Ivan Viola and Katja Buehler and Timo Ropinski
    Biological multi-channel microscopy data are often characterized by a high local entropy and phenotypically identical structures covering only a few pixels and forming disjoint regions spread over, e.g., a cell or a tissue section. Toponome data as an example, comprise a fluorescence image (channel) per protein affinity reagent, and capture the location and spatial distribution of proteins in cells and tissues. Biologists investigate such data using a region-of-interest in an image view and a linked view displaying information aggregated or derived from the channels. The cognitive effort of moving the attention back and forth between the views is immense. We present an approach for the in-place annotation of multi-channel microscopy data in 2D views. We combine dynamic excentric labeling and static necklace maps to cope with the special characteristics of these data. The generated annotations support the biologists in visually exploring multi-channel information directly in its spatial context. A label is generated per unique phenotype included in a flexible, moveable focus region. The labels are organized in a circular fashion around the focus region. On demand, a nested labeling can be generated by displaying a second ring of labels which represents the channels characterizing the focused phenotypes. We demonstrate our approach by toponome data of a rhabdomyosarcoma cell line and a prostate tissue section.
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    Real-Time Dense Nucleus Selection from Confocal Data
    (The Eurographics Association, 2014) Wan, Yong; Otsuna, Hideo; Kwan, K. M.; Hansen, Charles; Ivan Viola and Katja Buehler and Timo Ropinski
    Selecting structures from volume data using direct over-the-visualization interactions, such as a paint brush, is perhaps the most intuitive method in a variety of application scenarios. Unfortunately, it seems difficult to design a universal tool that is effective for all different structures in biology research. In [WOCH12b], an interactive technique was proposed for extracting neural structures from confocal microscopy data. It uses a dual-stroke paint brush to select desired structures directly from volume visualizations. However, the technique breaks down when it was applied to selecting densely packed structures with condensed shapes, such as nuclei from zebrafish eye development research. We collaborated with biologists studying zebrafish eye development and adapted the paint brush tool for real-time nucleus selection from volume data. The morphological diffusion algorithm used in the previous paint brush is restricted to gradient descending directions for improved nucleus boundary definition. Occluded seeds are removed using backward ray-casting. The adapted paint brush is then used in tracking cell movements in a time sequence dataset of a developing zebrafish eye.
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    Visual and Quantitative Analysis of Higher Order Arborization Overlaps for Neural Circuit Research
    (The Eurographics Association, 2014) Swoboda, Nicolas; Moosburner, Judith; Bruckner, Stefan; Yu, Jai Y.; Dickson, Barry J.; Bühler, Katja; Ivan Viola and Katja Buehler and Timo Ropinski
    Neuroscientists investigate neural circuits in the brain of the common fruit fly Drosophila melanogaster to discover how complex behavior is generated. Hypothesis building on potential connections between individual neurons is an essential step in the discovery of circuits that govern a specific behavior. Overlaps of arborizations of two or more neurons indicate a potential anatomical connection, i.e. the presence of joint synapses responsible for signal transmission between neurons. Obviously, the number of higher order overlaps (i.e. overlaps of three and more arborizations) increases exponentially with the number of neurons under investigation making it almost impossible to precompute quantitative information for all possible combinations. Thus, existing solutions are restricted to pairwise comparison of overlaps as they are relying on precomputed overlap quantification. Analyzing overlaps by visual inspection of more than two arborizations in 2D sections or in 3D is impeded by visual clutter or occlusion. This work contributes a novel tool that complements existing methods for potential connectivity exploration by providing for the first time the possibility to compute and visualize higher order arborization overlaps on the fly and to interactively explore this information in its spatial anatomical context and on a quantitative level. Qualitative evaluation with neuroscientists and non-expert users demonstrated the utility and usability of the tool.