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Documents authored by Schultz, Thomas

Document
DOI: 10.4230/DagRep.8.10.148
Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)

Authors: Andrea Fuster, Evren Özarslan, Thomas Schultz, and Eugene Zhang

Published in: Dagstuhl Reports, Volume 8, Issue 10 (2019)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18442, "Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy", which was attended by 30 international researchers, both junior and senior. Directional preferences or anisotropies are encountered across many different disciplines and spatial scales. These disciplines share a need for modeling, processing, and visualizing anisotropic quantities, which poses interesting challenges to applied computer science. With the goal of identifying open problems, making practitioners aware of existing solutions, and discovering synergies between different applications in which anisotropy arises, this seminar brought together researchers working on different aspects of computer science with experts from neuroimaging and astronomy. This report gathers abstracts of the talks held by the participants, as well as an account of topics raised within the breakout sessions.
Cite as

Andrea Fuster, Evren Özarslan, Thomas Schultz, and Eugene Zhang. Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442). In Dagstuhl Reports, Volume 8, Issue 10, pp. 148-172, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{fuster_et_al:DagRep.8.10.148,
  author =	{Fuster, Andrea and \"{O}zarslan, Evren and Schultz, Thomas and Zhang, Eugene},
  title =	{{Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy (Dagstuhl Seminar 18442)}},
  pages =	{148--172},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{10},
  editor =	{Fuster, Andrea and \"{O}zarslan, Evren and Schultz, Thomas and Zhang, Eugene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.8.10.148},
  URN =		{urn:nbn:de:0030-drops-103524},
  doi =		{10.4230/DagRep.8.10.148},
  annote =	{Keywords: Anisotropy, astronomy, diffusion-weighted imaging (DWI), geometry processing, tensor fields, topology, visualization, uncertainty, shape modeling, microstructure imaging, statistical analysis}
}
Document
DOI: 10.4230/DagRep.6.4.16
Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142)

Authors: Ingrid Hotz, Evren Özarslan, and Thomas Schultz

Published in: Dagstuhl Reports, Volume 6, Issue 4 (2016)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 16142, "Multidisciplinary Approaches to Multivalued Data: Modelling, Visualization, Analysis", which was attended by 27 international researchers, both junior and senior. Modelling multivalued data using tensors and higher-order descriptors has become common practice in neuroscience, engineering, and medicine. Novel tools for image analysis, visualization, as well as statistical hypothesis testing and machine learning are required to extract value from such data, and can only be developed within multidisciplinary collaborations. This report gathers abstracts of the talks held by participants on recent advances and open questions related to these challenges, as well as an account of topics raised within two of the breakout sessions.
Cite as

Ingrid Hotz, Evren Özarslan, and Thomas Schultz. Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142). In Dagstuhl Reports, Volume 6, Issue 4, pp. 16-38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{hotz_et_al:DagRep.6.4.16,
  author =	{Hotz, Ingrid and \"{O}zarslan, Evren and Schultz, Thomas},
  title =	{{Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142)}},
  pages =	{16--38},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{4},
  editor =	{Hotz, Ingrid and \"{O}zarslan, Evren and Schultz, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.6.4.16},
  URN =		{urn:nbn:de:0030-drops-61517},
  doi =		{10.4230/DagRep.6.4.16},
  annote =	{Keywords: visualization, image processing, statistical analysis, machine learning, tensor fields, higher-order descriptors, diffusion-weighted imaging (DWI), structural mechanics, fluid dynamics, microstructure imaging, connectomics, uncertainty visualization, feature extraction}
}
Document
DOI: 10.4230/DFU.Vol2.SciViz.2011.322
Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond

Authors: Thomas Schultz

Published in: Dagstuhl Follow-Ups, Volume 2, Scientific Visualization: Interactions, Features, Metaphors (2011)

Abstract
By measuring the anisotropic self-diffusion rates of water, Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) provides a unique noninvasive probe of fibrous tissue. In particular, it has been explored widely for imaging nerve fiber tracts in the human brain. Geometric features provide a quick visual overview of the complex datasets that arise from DW-MRI. At the same time, they build a bridge towards quantitative analysis, by extracting explicit representations of structures in the data that are relevant to specific research questions. Therefore, features in DWMRI data are an active research topic not only within scientific visualization, but have received considerable interest from the medical image analysis, neuroimaging, and computer vision communities. It is the goal of this paper to survey contributions from all these fields, concentrating on streamline clustering, edge detection and segmentation, topological methods, and extraction of anisotropy creases. We point out interrelations between these topics and make suggestions for future research.
Cite as

Thomas Schultz. Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond. In Scientific Visualization: Interactions, Features, Metaphors. Dagstuhl Follow-Ups, Volume 2, pp. 322-345, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InCollection{schultz:DFU.Vol2.SciViz.2011.322,
  author =	{Schultz, Thomas},
  title =	{{Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond}},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{322--345},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol2.SciViz.2011.322},
  URN =		{urn:nbn:de:0030-drops-33010},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.322},
  annote =	{Keywords: Diffusion-Weighted MRI, dMRI, DT-MRI, DTI, HARDI, Streamline Clustering, Edge Detection, DW-MRI Segmentation, Tensor Topology, Crease Surfaces}
}
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