@inbook {937,
	title = {Case Studies for Working with Domain Experts},
	booktitle = {Foundations of Data Visualization},
	year = {2020},
	pages = {255{\textendash}278},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The collaboration with domain experts concentrates always on an application domain where the experts work. Usually, they provide the data and directions of research that require visualization support. This chapter presents seven successful cases of such collaborations. The domain varies from biology and medicine to mechanical engineering. There are examples of long time cooperation as well as smaller short-term projects. The description concentrates on the process, output, and especially on the lessons learnt from these cooperations. The scientific work is described to understand the context and goals of the cooperation, but many details can only be found in the references. The reason for this unusual writing is the wish on the one hand to describe various aspects of collaboration with domain experts which is an important part of the foundations of data visualization. On the other hand, the text should not become lengthy and filled with too many details of individual cases that can be found elsewhere.},
	isbn = {978-3-030-34444-3},
	doi = {10.1007/978-3-030-34444-3_13},
	url = {https://doi.org/10.1007/978-3-030-34444-3_13},
	author = {Beyer, Johanna and Hansen, Charles and Hlawitschka, Mario and Hotz, Ingrid and Kozl{\'\i}kov{\'a}, Barbora and Scheuermann, Gerik and Stommel, Markus and Streit, Marc and Waschke, Johannes and Wischgoll, Thomas and Wan, Yong},
	editor = {Chen, Min and Hauser, Helwig and Rheingans, Penny and Scheuermann, Gerik}
}
@conference {936,
	title = {Uncertainty-aware Brain Lesion Visualization},
	booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
	year = {2020},
	publisher = {The Eurographics Association},
	organization = {The Eurographics Association},
	isbn = {978-3-03868-109-0},
	doi = {10.2312/vcbm.20201176},
	author = {Gillmann, Christina and Saur, Dorothee and Wischgoll, Thomas and Hoffmann, Karl-Titus and Hagen, Hans and Maciejewski, Ross and Scheuermann, Gerik},
	editor = {Kozl{\'\i}kov{\'a}, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia}
}
@article {853,
	title = {Hierarchical Image Semantics using Probabilistic Path Propagations for Biomedical Research},
	journal = {IEEE computer graphics and applications},
	volume = {39},
	year = {2019},
	abstract = {Image segmentation is an important subtask in biomedical research applications, such as estimating the position and shape of a tumor. Unfortunately, advanced image segmentation methods are not widely applied in research applications as they often miss features, such as uncertainty communication, and may lack an intuitive approach for the use of the underlying algorithm. To solve this problem, this paper fuses a fuzzy and a hierarchical segmentation approach together, thus providing a flexible multiclass segmentation method based on probabilistic path propagations. By utilizing this method, analysts and physicians can map their mental model of image components and their composition to higher level objects. The probabilistic segmentation of higher order components is propagated along the user-defined hierarchy to highlight the potential of improvement resulting in each level of hierarchy by providing an intuitive representation. The effectiveness of this approach is
demonstrated by evaluating our segmentations of biomedical datasets, comparing it to the state-of-the-art segmentation approaches, and an extensive user study.},
	author = {Gillmann, Christina and Post, Tobias and Wischgoll, Thomas and Hagen, Hans and Maciejewski, Ross}
}
@article {857,
	title = {Accurate and reliable extraction of surfaces from image data using a multi-dimensional uncertainty model},
	volume = {Volume 99},
	year = {2018},
	chapter = {Pages 13},
	abstract = {Surface extraction is an important step in the image processing pipeline to estimate the size and shape of an object. Unfortunately, state of the art surface extraction algorithms form a straight forward extraction based on a pre-defined value that can lead to surfaces, that are not accurate. Furthermore, most isosurface extraction algorithms lack the ability to communicate uncertainty originating from the image data. This can lead to a rejection of such algorithms in many applications. To solve this problem, we propose a methodology to extract and optimize surfaces from image data based on a defined uncertainty model. To identify optimal parameters, the presented method defines a parameter space that is evaluated and rates each extraction run based on the remaining surface uncertainty. The resulting surfaces can be explored intuitively in an interactive framework. We applied our methodology to a variety of datasets to demonstrate the quality of the resulting surfaces.},
	author = {Gillmann, Christina and Wischgoll, Thomas and Hamann, Bernd and Hagen, Hans}
}
@conference {855,
	title = {Modeling and Visualization of Uncertainty-aware Geometry using Multi-variate Normal Distributions},
	booktitle = {IEEE Pacific Vis Short Paper Track},
	year = {2018},
	abstract = {Many applications are dealing with geometric data that are affected by uncertainty. This uncertainty is important to analyze, visualize, and understand. We present a methodology to model uncertain geometry based on multi-variate normal distributions. In addition, we propose a visualization technique to represent a hull for uncertain geometry capturing a user-defined percentage of the underlying uncertain geometry. To show the effectiveness of our approach, we have modeled and visualized uncertain datasets from different applications.},
	author = {Gillmann, Christina and Wischgoll, Thomas and Hamann, Bernd and Ahrens James}
}
@article {859,
	title = {Towards an Image-based Indicator for Peripheral Artery Disease Classification and Localization},
	journal = {LEVIA{\textquoteright}18 : Leipzig Symposium on Visualization in Applications},
	year = {2018},
	abstract = {
Peripheral Artery Disease (PAD) is an often occurring problem caused by narrowed veins. With this type of disease, mostly the legs receive an insufficient supply of blood to sustain their functions. This can result in an amputation of extremities or strokes. In order to quantify the risks, doctors consult a classification table which is based on the pain response of a patient. This classification is subjective and does not indicate the exact origin of the PAD symptoms. Resulting from this, complications can occur unprompted. We present the first results for an image-based indicator assisting medical doctors in estimating the stage of PAD and its location. Therefore, a segmentation tree is utilized to compare the changes in a healthy versus diseased leg. We provide a highlighting mechanism that allows users to review the location of changes in selected structures. To show the effectiveness of the presented approach, we demonstrate a localization of the PAD and show how the presented technique can be utilized for a novel image-based indicator of PAD stages.},
	author = {Gillmann, Christina and Matsuura, John and Hagen, Hans and Wischgoll, Thomas}
}
@article {856,
	title = {An uncertainty-aware visual system for image pre-processing},
	journal = {Journal of Imaging},
	volume = {4},
	year = {2018},
	abstract = {Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image{\textquoteright}s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets.},
	author = {Gillmann, Christina and Arbelaez, Pablo and Hernandez, Jose Tiberio and Hagen, Hans and Wischgoll, Thomas}
}
@article {858,
	title = {An uncertainty-aware workflow for keyhole surgery planning using hierarchical image semantics},
	journal = {Visual Informatics},
	volume = {2},
	year = {2018},
	abstract = {Keyhole surgeries become increasingly important in clinical daily routine as they help minimizing the damage of a patient{\textquoteright}s healthy tissue. The planning of keyhole surgeries is based on medical imaging and an important factor that influences the surgeries{\textquoteright} success. Due to the image reconstruction process, medical image data contains uncertainty that exacerbates the planning of a keyhole surgery. In this paper we present a visual workflow that helps clinicians to examine and compare different surgery paths as well as visualizing the patients{\textquoteright} affected tissue. The analysis is based on the concept of hierarchical image semantics, that segment the underlying image data with respect to the input images{\textquoteright} uncertainty and the users understanding of tissue composition. Users can define arbitrary surgery paths that they need to investigate further. The defined paths can be queried by a rating function to identify paths that fulfill user-defined properties. The workflow allows a visual inspection of the affected tissues and its substructures. Therefore, the workflow includes a linked view system indicating the three-dimensional location of selected surgery paths as well as how these paths affect the patients tissue. To show the effectiveness of the presented approach, we applied it to the planning of a keyhole surgery of a brain tumor removal and a kneecap surgery.},
	author = {Gillmann, Christina and Maack, Robin and Post, Tobias and Wischgoll, Thomas and Hagen, Hans}
}
@conference {860,
	title = {Visual Analytics of Cascaded Bottlenecks in Planar Flow Networks},
	booktitle = {LEVIA 2018},
	year = {2018},
	abstract = {
Finding bottlenecks and eliminating them to increase the overall flow of a network often appears in real world applications, such as production planning, factory layout, flow related physical approaches, and even cyber security. In many cases, several edges can form a bottleneck (cascaded bottlenecks). This work presents a visual analytics methodology to analyze these cascaded bottlenecks. The methodology consists of multiple steps: identification of bottlenecks, identification of potential improvements, communication of bottlenecks, interactive adaption of bottlenecks, and a feedback loop that allows users to adapt flow networks and their resulting bottlenecks until they are satisfied with the flow network configuration. To achieve this, the definition of a minimal cut is extended to identify network edges that form a (cascaded) bottleneck. To show the effectiveness of the presented approach, we applied the methodology to two flow network setups and show how the overall flow of these networks can be improved.},
	author = {Post, Tobias and Gillmann, Christina and Wischgoll, Thomas and Hamann, Bernd and Hagen, Hans}
}
@article {861,
	title = {An Industrial Vision System to Analyze the Wear of Cutting Tools},
	journal = {Applied Mechanics and Materials},
	volume = {869},
	year = {2017},
	abstract = {The wear behavior of cutting tools directly affects the quality of the machined part. The measurement and evaluation of wear is a time consuming and process and is subjective. Therefore, an image-based wear measure that can be computed automatically based on given image series of cutting tools and an objective way to review the resulting wear is presented in this paper. The presented method follows the industrial vision system pipeline where images of cutting tools are used as input which are then transformed through suitable image processing methods to prepare them for the computation of a novel image based wear measure. For multiple cutting tool settings a comparative visualization of the wear measure outputs is presented. The effectiveness of the presented approach is shown by applying the method to measure the wear of four different cutting tool shapes.},
	author = {Gillmann, Christina and Post, Tobias and Kirsch, Benjamin and Wischgoll, Thomas and J{\"o}rg, Hartig and Hamann, Bernd and Hagen, Hans and Aurich, Christian}
}
@conference {862,
	title = {Intuitive Error Space Exploration of Medical Image Data in Clinical Daily Routine},
	booktitle = {EuroVis 2017 Short Paper Track},
	year = {2017},
	abstract = {Medical image data can be affected by several image errors. These errors can lead to uncertain or wrong diagnosis in clinical daily routine. A large variety of image error metrics are available that target different aspects of image quality forming a highdimensional error space, which cannot be reviewed trivially. To solve this problem, this paper presents a novel error space exploration technique that is suitable for clinical daily routine. Therefore, the clinical workflow for reviewing medical data is extended by error space cluster information, that can be explored by user-defined selections. The presented tool was applied to two real-world datasets to show its effectiveness.},
	author = {Gillmann, Christina and Arbelaez, Pablo and Hernandez, Jose Tiberio and Hagen, Hans and Wischgoll, Thomas}
}
@conference {864,
	title = {Fast 3D Thinning of Medical Image Data based on Local Neighborhood Lookups},
	booktitle = {EuroVis (Short Papers)},
	year = {2016},
	abstract = {Three-dimensional thinning is an important task in medical image processing when performing quantitative analysis on structures, such as bones and vessels. For researchers of this domain a fast, robust and easy to access implementation is required.
The Insight Segmentation and Registration Toolkit (ITK) is often used in medical image processing and visualization as it offers
a wide range of ready to use algorithms. Unfortunately, its thinning implementation is computationally expensive and can introduce errors in the thinning process. This paper presents an implementation that is ready to use for thinning of medical image
data. The implemented algorithm evaluates a moving local neighborhood window to find deletable voxels in the medical image.
To reduce the computational effort, all possible combinations of a local neighborhood are stored in a precomputed lookup table.
To show the effectiveness of this approach, the presented implementation is compared to the performance of the ITK library.},
	author = {Post, Tobias and Gillmann, Christina and Wischgoll, Thomas and Hagen, Hans}
}
@conference {863,
	title = {From theory to usage: Requirements for successful visualizations in applications},
	booktitle = {IEEE Visualization Conference (VIS)-C4PGV Workshop},
	year = {2016},
	abstract = {Visualizations are a powerful tool to solve various tasks in different applications. Although a huge variety of visualization techniques are constantly published, only a few of them end up being used in real world day-to-day operations. To identify the reasons for this observation, this work aims at summarizing the criteria, that promote a real world application of a visualization tool.},
	author = {Gillmann, Christina and Leitte, Heike and Wischgoll, Thomas and Hagen, Hans}
}
@conference {866,
	title = {OpenThinning: Fast 3D Thinning based on Local Neighborhood Lookups},
	booktitle = {Vis in Practice 2016},
	year = {2016},
	abstract = {3D Thinning is an often required image processing task in order to perform shape analysis in various applications. For researchers in these domains, a fast, flexible and easy to access implementation is required. Open source solutions, as the Insights Segmentation and Registration Toolkit (ITK), are often used for image processing and visualization tasks, due to their wide range of provided algorithms. Unfortunately, ITK{\textquoteright}s thinning implementation is computational expensive and allows solely one specific thinning approach. Therefore, this work presents OpenThinning, an open source thinning solution for 3D image data. The implemented algorithm evaluates a moving local neighborhood to find deletable voxels, according to different sets of criteria. In order to reduce the computational effort, all possible local neighborhood setting outputs are stored in a lookup table. To show the effectiveness of OpenThinning, the implementation is compared to the performance of the ITK library.},
	author = {Post, Tobias and Gillmann, Christina and Wischgoll, Thomas and Hagen, Hans}
}
@conference {865,
	title = {Uncertainty-awareness in open source visualization solutions},
	booktitle = {IEEE Vis 2016 Workshop on Visualization in Practice},
	year = {2016},
	abstract = {The popularity of open source tools is constantly increasing, as they offer the possibility to quickly create and use visualizations of arbitrary data sources. As the positive effects of uncertainty communication to all kinds of visualizations were discussed over the past years in the academic world, this work examines the uncertaintyawareness of open source solutions. Through a categorization and classification of available tools, this paper identifies the problems in uncertainty-awareness of available open source solutions. To solve this problem, a new paradigm of data handling that extends raw datasets by its uncertainty is suggested.},
	author = {Gillmann, Christina and Wischgoll, Thomas and Hagen, Hans}
}
