Browsing by Author "Gillmann, Christina"
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Item EuroVis 2023 Posters: Frontmatter(The Eurographics Association, 2023) Gillmann, Christina; Krone, Michael; Lenti, Simone; Gillmann, Christina; Krone, Michael; Lenti, SimoneItem Uncertainty-aware Brain Lesion Visualization(The Eurographics Association, 2020) Gillmann, Christina; Saur, Dorothee; Wischgoll, Thomas; Hoffmann, Karl-Titus; Hagen, Hans; Maciejewski, Ross; Scheuermann, Gerik; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaA brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed for brain lesions. Our method is based on an uncertainty measure for image data that forms the input of an uncertainty-aware segmentation approach. Here, medical doctors can determine the lesion in the patient's brain and the result can be visualized by an uncertainty-aware geometry rendering. We applied our approach to two patient datasets to review the lesions. Our results indicate increased knowledge discovery in brain lesion analysis that provides a quantification of trust in the generated results.Item Uncertainty-aware Detection and Visualization of Ocean Eddies in Ensemble Flow Fields - A Case Study of the Red Sea(The Eurographics Association, 2021) Raith, Felix; Scheuermann, Gerik; Gillmann, Christina; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, DirkEddy detection is a state of the art tool to examine transport behavior in oceans, as they form circular movements that are highly involved in transferring mass in an ocean. To achieve this, ocean simulations are run multiple times, and an eddy detection is performed in the final simulation results. Unfortunately, this process is affected by a variety of uncertainties. In this manuscript, we aim to identify the types of uncertainty inherent in ocean simulations. For each of the identified uncertainties, we provide a quantification approach. Based on the quantified uncertainties, we provide a visualization approach that consists of domain embedded views and an uncertainty space view connected via interaction. We showed the effectiveness of our approach by performed a case study of the Red Sea.Item Uncertainty-aware Visualization in Medical Imaging - A Survey(The Eurographics Association and John Wiley & Sons Ltd., 2021) Gillmann, Christina; Saur, Dorothee; Wischgoll, Thomas; Scheuermann, Gerik; Smit, Noeska and Vrotsou, Katerina and Wang, BeiMedical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty-aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty-aware medical imaging.Item Understanding Graph Convolutional Networks to detect Brain Lesions from Stroke(The Eurographics Association, 2022) Iporre-Rivas, Ariel; Scheuermann, Gerik; Gillmann, Christina; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun WuBrain lesions derived from stroke episodes can result in disabilities for a patient. Therefore, the segmentation of brain lesions is an important task in neurology. Recently this task has been mainly tackled by machine learning approaches that demonstrated to be very successful. One of these approaches is Graph Convolutional Networks (GCN), where the input image is interpreted as a graph structure. As usual for neural networks, the interpretability is hard due to their black-box nature. We provide an interactive visualization of the activation inherent in the GCN, which is map from the original dataset. We visualize the activation values of the underlying graph network on top of the input image. We show the usability of our approach by applying it to a GCN that was trained on a real-world dataset.Item VisGap 2020: Frontmatter(The Eurographics Association, 2020) Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, Thomas; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, ThomasItem VisGap 2021: Frontmatter(The Eurographics Association, 2021) Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, Thomas; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, ThomasItem VisGap 2022: Frontmatter(The Eurographics Association, 2022) Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, Thomas; Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, ThomasItem VisGap 2023: Frontmatter(The Eurographics Association, 2023) Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, Thomas; Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, ThomasItem Visualization Framework for Assisting Interface Optimization of Hybrid Component Design(The Eurographics Association, 2020) Kretzschmar, Vanessa; Gillmann, Christina; Günther, Fabian; Stommel, Markus; Scheuermann, Gerik; Krüger, Jens and Niessner, Matthias and Stückler, JörgReliable component design is one of structural mechanics' main objectives. Especially for lightweight constructions, hybrid parts made of a multi-material combination are used. The design process for these parts often becomes very challenging. The critical section of such hybrid parts is usually the interface layer that often builds the weakest zone. In this paper, we study a hybrid part made of metal and carbon fiber-reinforced composite, where the metal insert is coated by a thermoplastic to decrease the jump in stiffness between the two primary structural materials. To prevent stress peaks in small volumes of the part , mechanical engineers aim to design functional elements at the thermoplastic interface, to homogenize the stress distribution. The placement of such load transmitting functional elements at the thermoplastics interface has a crucial impact on the overall stability and mechanical performance of the design. Resulting from this, mechanical engineers acquire large amounts of simulations outputting multi-field datasets, to examine the impact of differently designed load transmitting elements, their number, and positioning in the interface between metal and composite. In order to assist mechanical engineers in deeper exploration of the often numerous set of simulations, a framework based on visual analytics techniques was developed in close collaboration with engineers. To match their needs, a requirement analysis was performed, and visualizations were discussed steadily. We show how the presented framework helps engineers gaining novel insights to optimize the hybrid component based on the selected load transmitting elements.