EuroRVVV16

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https://diglib.eg.org/handle/10.2312/15278

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Browsing EuroRVVV16 by Title

Now showing 1 - 10 of 10
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    Classifying Medical Projection Techniques based on Parameterization Attribute Preservation
    (The Eurographics Association, 2016) Kreiser, Julian; Ropinski, Timo; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    In many areas of medicine, visualization researchers can help by contributing to to task simplification, abstraction or complexity reduction. As these approaches, can allow a better workflow in medical environments by exploiting easier communication through visualization, it is important to question their reliability and their reproducibility. Therefore, within this short paper, we investigate how projections used in medical visualization, can be classified with respect to the handled data and the underlying tasks. Many of these techniques are inspired by mesh parameterization, which allows for reducing a surface from R3 to R2. This makes complex structures often easier to understand by humans and machines. In the following section, we will classify different algorithms in this area (see Table 1) and discuss how these mappings benefit medical visualization.
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    Detection of Diabetic Neuropathy - Can Visual Analytics Methods Really Help in Practice?
    (The Eurographics Association, 2016) Röhlig, Martin; Stachs, Oliver; Schumann, Heidrun; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    Visual analytics (VA) methods are valuable means for supporting the detection of diabetic neuropathy, the most common longterm complication of diabetes mellitus. We suggest two strategies for strengthening reliability, reproducibility, and applicability of dedicated VA methods in practice. First, we introduce a novel workflow visualization that shows activities together with metadata and produced output, facilitating a guided step-wise analysis. Second, we present a tailored user interface that integrates various VA tools, unifying access to their functionality and enabling free exploration for further assisting the medical diagnosis. By applying both strategies, we effectively enhance the practical utility of our VA approach for detecting diabetic neuropathy.
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    EuroRV3 2016: Frontmatter
    (Eurographics Association, 2016) Kai Lawonn; Mario Hlawitschka; Paul Rosenthal;
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    Experiences on Validation of Multi-Component System Simulations for Medical Training Applications
    (The Eurographics Association, 2016) Law, Yuen C.; Weyers, Benjamin; Kuhlen, Torsten W.; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    In the simulation of multi-component systems, we often encounter a problem with a lack of ground-truth data. This situation makes the validation of our simulation methods and models a difficult task. In this work we present a guideline to design validation methodologies that can be applied to the validation of multi-component simulations that lack of ground-truth data. Additionally we present an example applied to an Ultrasound Image Simulation for medical training and give an overview of the considerations made and the results for each of the validation methods. With these guidelines we expect to obtain more comparable and reproducible validation results from which other similar work can benefit.
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    High-Performance Motion Correction of Fetal MRI
    (The Eurographics Association, 2016) Kainz, Bernhard; Lloyd, David F. A.; Alansary, Amir; Murgasova, Maria Kuklisova; Khlebnikov, Rostislav; Rueckert, Daniel; Rutherford, Mary; Razavi, Reza; Hajnal, Jo V.; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    Fetal Magnetic Resonance Imaging (MRI) shows promising results for pre-natal diagnostics. The detection of potentially lifethreatening abnormalities in the fetus can be difficult with ultrasound alone. MRI is one of the few safe alternative imaging modalities in pregnancy. However, to date it has been limited by unpredictable fetal and maternal motion during acquisition. Motion between the acquisitions of individual slices of a 3D volume results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms to solve this problem have evolved from very slow implementations targeting a single organ to general high-performance solutions to reconstruct the whole uterus. In this paper we give a brief overview over the current state-of-the art in fetal motion compensation methods and show currently emerging clinical applications of these techniques.
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    An Introduction to Evaluation in Medical Visualization
    (The Eurographics Association, 2016) Smit, Noeska; Lawonn, Kai; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    Medical visualization papers often deal with data that is interpreted by medical domain experts in a research or clinical context. Since visualizations are by definition designed to be interpreted by a human observer, often an evaluation is performed to confirm the utility of a presented method. The exact type of evaluation required is not always clear, especially to new researchers. With this paper, we hope to clarify the different types of evaluation methods that exist and provide practical guidelines to choose the most suitable evaluation method to increase the value of the work.
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    On the Evaluation of a Semi-Automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta
    (The Eurographics Association, 2016) Meuschke, Monique; Köhler, Ben; Preim, Bernhard; Lawonn, Kai; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    In this paper, we report on our experiences that we made during our contributions in the field of the visualization of flow characteristics. Mainly, we focused on the vortex flow classification in 4D PC-MRI as current medical studies assume a strong correlation between cardiovascular diseases and blood flow patterns such as vortices. For further analysis, medical experts are asked to manually extract and classify such vortices according to specific properties. We presented and evaluated techniques that enable a fast and robust vortex classification [MLK 16,MKP 16] that supports medical experts. The main focus in this paper is a report that describes our conversations with the domain experts. The dialog was the fundament that gave us the direction of what the experts need. We derived several requirements that should be fulfilled by our tool. From this, we developed a prototype that supports the experts. Finally, we describe the evaluation of our framework and discuss currently limitations.
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    OphthalVis - Making Data Analytics of Optical Coherence Tomography Reproducible
    (The Eurographics Association, 2016) Rosenthal, Paul; Ritter, Marc; Kowerko, Danny; Heine, Christian; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    In this paper, we discuss the issues of the current state of the art in optical coherence tomography with respect to reproducibility. We present our findings about the internal computations and data storage methods of the currently used devices. The gained knowledge was used to implement a tool to read a variety of OCT file formats and reproduce the visualizations used in daily clinical routine.
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    Towards Multi-user Provenance Tracking of Visual Analysis Workflows over Multiple Applications
    (The Eurographics Association, 2016) Hänel, Claudia; Khatami, Mohammad; Kuhlen, Torsten W.; Weyers, Benjamin; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    Provenance tracking for visual analysis workflows is still a challenge as especially interaction and collaboration aspects are poorly covered in existing realizations. Therefore, we propose a first prototype addressing these issues based on the PROV model. Interactions in multiple applications by multiple users can be tracked by means of a web interface and, thus, allowing even for tracking of remote-located collaboration partners. In the end, we demonstrate the applicability based on two use cases and discuss some open issues not addressed by our implementation so far but that can be easily integrated into our architecture.
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    Uncertainty and Reproducibility in Medical Visualization
    (The Eurographics Association, 2016) Linsen, Lars; Al-Taie, Ahmed; Ristovski, Gordan; Preusser, Tobias; Hahn, Horst K.; Kai Lawonn and Mario Hlawitschka and Paul Rosenthal
    The medical visualization pipeline is affected by various sources of uncertainty. Many errors may occur and several assumptions are made in the various processing steps from the image acquisition to the rendering of the visualization output, which induce uncertainty. High uncertainty leads to low robustness of the algorithms impacting reproducibility of the results. We present how uncertainty can be mathematically described in the medical context. Moreover, in medical applications, the visualization is typically based on a segmentation of the medical images. We propose a method to capture uncertainty in image segmentation and present extensions to ensemble and multi-modal image segmentation.