Dirk-Bartz-Prize 2021

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    EuroVis 2021 Dirk Bartz Prize: Frontmatter
    (The Eurographics Association, 2021) Oeltze-Jafra, Steffen; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
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    Visual Exploration of Intracranial Aneurysm Blood Flow Adapted to the Clinical Researcher
    (The Eurographics Association, 2021) Behrendt, Benjamin; Engelke, Wito; Berg, Philipp; Beuing, Oliver; Hotz, Ingrid; Preim, Bernhard; Saalfeld, Sylvia; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
    Rupture risk assessment is a key to devise patient-specific treatment plans of cerebral aneurysms. To understand and predict the development of aneurysms and other vascular diseases over time, both hemodynamic flow patterns and their effect on the vessel surface need to be analyzed. Flow structures close to the vessel wall often correlate directly with local changes in surface parameters, such as pressure or wall shear stress. However, especially for the identification of specific blood flow characteristics that cause local startling parameters on the vessel surface, like elevated pressure values, an interactive analysis tool is missing. In order to find meaningful structures in the entirety of the flow, the data has to be filtered based on the respective explorative aim. Thus, we present a combination of visualization, filtering and interaction techniques for explorative analysis of blood flow with a focus on the relation of local surface parameters and underlying flow structures. In combination with a filtering-based approach, we propose the usage of evolutionary algorithms to reduce the overhead of computing pathlines that do not contribute to the analysis, while simultaneously reducing the undersampling artifacts. We present clinical cases to demonstrate the benefits of both our filter-based and evolutionary approach and showcase its potential for patient-specific treatment plans.
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    Visual Assistance in Clinical Decision Support
    (The Eurographics Association, 2021) Müller, Juliane; Cypko, Mario; Oeser, Alexander; Stoehr, Matthäus; Zebralla, Veit; Schreiber, Stefanie; Wiegand, Susanne; Dietz, Andreas; Oeltze-Jafra, Steffen; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
    Clinical decision-making for complex diseases such as cancer aims at finding the right diagnosis, optimal treatment or best aftercare for a specific patient. The decision-making process is very challenging due to the distributed storage of patient information entities in multiple hospital information systems, the required inclusion of multiple clinical disciplines with their different views of disease and therapy, and the multitude of available medical examinations, therapy options and aftercare strategies. Clinical Decision Support Systems (CDSS) address these difficulties by presenting all relevant information entities in a concise manner and providing a recommendation based on interdisciplinary disease- and patient-specific models of diagnosis and treatment. This work summarizes our research on visual assistance for therapy decision-making. We aim at supporting the preparation and implementation of expert meetings discussing cancer cases (tumor boards) and the aftercare consultation. In very recent work, we started to address the generation of models underlying a CDSS. The developed solutions combine state-of-the-art interactive visualizations with methods from statistics, machine learning and information organization.
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    Visual Analysis of Tissue Images at Cellular Level
    (The Eurographics Association, 2021) Somarakis, Antonios; Ijsselsteijn, Marieke E.; Kenkhuis, Boyd; Unen, Vincent van; Luk, Sietse J.; Koning, Frits; Weerd, Louise van der; Miranda, Noel F. C. C. de; Lelieveldt, Boudewijn P. F.; Höllt, Thomas; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
    The detailed analysis of tissue composition is crucial for the understanding of tissue functionality. For example, the location of immune cells related to a tumour area is highly correlated with the effectiveness of immunotherapy. Therefore, experts are interested in presence of cells with specific characteristics as well as the spatial patterns they form. Recent advances in single-cell imaging modalities, producing high-dimensional, high-resolution images enable the analysis of both of these features. However, extracting useful insight on tissue functionality from these high-dimensional images poses serious and diverse challenges to data analysis. We have developed an interactive, data-driven pipeline covering the main analysis challenges experts face, from the pre-processing of images via the exploration of tissue samples to the comparison of cohorts of samples. All parts of our pipeline have been developed in close collaboration with domain experts and are already a vital part in their daily analysis routine.