2023
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@article{mittenentzwei2023heros,
journal = {Computer Graphics Forum},
title = {{Do Disease Stories need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease}},
author = {Mittenentzwei, Sarah and Weiß, Veronika and Schreiber, Stefanie and Garrison, Laura A. and Bruckner, Stefan and Pfister, Malte and Preim, Bernhard and Meuschke, Monique},
year = {2023},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14817},
abstract = {Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand-crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data-driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character-centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist's influence on user experience in terms of engagement, identification, self-referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character-driven story structure for disease stories derived from Joseph Campbell's Hero's Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.},
pdf = {pdfs/garrison-diseasestories.pdf},
images = {images/garrison-diseasestories.png},
thumbnails = {images/garrison-diseasestories-thumb.png}
}
[Bibtex]
@incollection{garrison2023narrativemedvisbook,
title = {Current Approaches in Narrative Medical Visualization},
author = {Garrison, Laura Ann and Meuschke, Monique and Preim, Bernhard and Bruckner, Stefan},
year = 2023,
booktitle = {Approaches for Science Illustration and Communication},
publisher = {Springer},
address = {Gewerbestrasse 11, 6330 Cham, Switzerland},
pages = {},
note = {in publication},
editor = {Mark Roughley},
chapter = 4
}
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@article{garrison2023molaesthetics,
author={Garrison, Laura A. and Goodsell, David S. and Bruckner, Stefan},
journal={IEEE Computer Graphics and Applications},
title={Changing Aesthetics in Biomolecular Graphics},
year={2023},
volume={43},
number={3},
pages={94-101},
doi={10.1109/MCG.2023.3250680},
abstract={Aesthetics for the visualization of biomolecular structures have evolved over the years according to technological advances, user needs, and modes of dissemination. In this article, we explore the goals, challenges, and solutions that have shaped the current landscape of biomolecular imagery from the overlapping perspectives of computer science, structural biology, and biomedical illustration. We discuss changing approaches to rendering, color, human–computer interface, and narrative in the development and presentation of biomolecular graphics. With this historical perspective on the evolving styles and trends in each of these areas, we identify opportunities and challenges for future aesthetics in biomolecular graphics that encourage continued collaboration from multiple intersecting fields.},
pdf = {pdfs/garrison-aestheticsmol.pdf},
images = {images/garrison-aestheticsmol.png},
thumbnails = {images/garrison-aestheticsmol-thumb.png}
}
2022
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@phdthesis{moerth2022thesis,
title = {Scaling Up Medical Visualization: Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication},
author = {Mörth, Eric},
year = 2022,
month = {September},
isbn = 9788230862193,
url = {https://hdl.handle.net/11250/3014336},
school = {Department of Informatics, University of Bergen, Norway},
abstract = {
Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data.
This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition.
Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.
},
pdf = {pdfs/Moerth-PhD-Thesis-2022.pdf},
images = {images/Moerth-PhD-Thesis-2022.PNG},
thumbnails = {images/Moerth-PhD-Thesis-2022.PNG},
project = {ttmedvis}
}
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@inproceedings {Trautner-2022-HCP,
author = {Trautner, Thomas and Sbardellati, Maximilian and Stoppel, Sergej and Bruckner, Stefan},
title = {{Honeycomb Plots: Visual Enhancements for Hexagonal Maps}},
booktitle = {Proc. of VMV 2022: Vision, Modeling, and Visualization},
editor = {Bender, Jan and Botsch, Mario and Keim, Daniel A.},
pages = {65--73},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-189-2},
DOI = {10.2312/vmv.20221205},
abstract = {Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.},
pdf = "pdfs/Trautner-2022-HCP.pdf",
thumbnails = "images/Trautner-2022-HCP-thumb.png",
images = "images/Trautner-2022-HCP-thumb.png",
youtube = "https://youtu.be/mU7QFVP3yKQ",
git = "https://github.com/TTrautner/HoneycombPlots"
}
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@phdthesis{trautner2022thesis,
title = {Visualization Hybridization with Spatialization Cues},
author = {Thomas Bernhard Trautner},
year = 2022,
month = {November},
isbn = 9788230855515,
url = {https://hdl.handle.net/11250/3031041},
school = {Department of Informatics, University of Bergen, Norway},
abstract = {
Visualization as a tool for visual processing of any underlying data has proven to be an accepted and legitimate part of the scientific reasoning process. Many different techniques help gaining new insights from captured phenomena, support the development or evaluation of hypotheses about collected data, reveal potential misconceptions or false assumptions, simplify communicating knowledge and novel findings, and enable a multitude of additional opportunities. The reason for this effectiveness is that the human visual system is ideally suited to capture and process visually encoded data. The development of visualization from a niche to an established scientific field has made a significant contribution to this success story. A large number of journals, conferences, seminars, and workshops regularly publish new results, evaluate presented approaches, and help making knowledge globally accessible. However, this large number of contributions tailored to variable user groups, the underlying data, and the wide variety of tasks that could be performed with them, emphasizes the plethora of available techniques and the resulting difficulty in choosing the most suitable visualizations.
Therefore, we investigated common data sets and analyzed typical tasks normally performed with them. Based on this, we selected well-established and most effective visualization techniques, combining them to form a hybrid representation. The goal of such a visualization hybridization was to merge advantages of individual techniques and, thereby, simultaneously eliminate their limitations. We present so-called hybrid vigors that make the underlying visualizations more widely applicable instead of either having to change required techniques sequentially, or not being able to perform certain tasks at all. Our contributions are intended to simplify the process of finding suitable visualizations for already established data sets. During our research, we focused on two-dimensional point data, depicted on the one hand as scatter plots and, on the other hand, as relationships between consecutive point such as in line charts. Our techniques can be used especially when data sets are so large, dense, and overplotted that conventional techniques reach their limits. We show that hybrid representations are well suited for combining discrete, continuous, or aggregated forms of visual representation. Our hybridizations additionally exploit spatialization cues. Such visual cues emphasize spatiality of the underlying data through shading, without having to embed the data in 3D space including its potential disadvantages. We chose this method of encoding as we consider it the most appropriate choice, given that visualization users interact naturally and preattentively with a spatial world on a daily basis.
},
pdf = {pdfs/Trautner-PhD-Thesis-2022.pdf},
images = {images/Trautner-2022-PhD.png},
thumbnails = {images/Trautner-2022-PhD.png},
project = {MetaVis}
}