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"title": "Visual Analytics to Support Correlative Exploration and Sensemaking in Radiogenomics Analysis",
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"abstract": "Radiogenomics refers to the combined study of imaging-derived features, called radiomics and gene sequencing data, called genomics. Challenges in the analysis of radiogenomic data include the size, heterogeneity, and complexity of the datasets. These challenges make the analysis of the available information space tedious for cancer experts and hinder the exploration and sensemaking of patient information. This is further hampered when additional clinical information needs to be included in the analyses. Visual Analytics (VA) combines automated analysis techniques, such as machine learning or statistics, together with interactive visual interfaces. It allows users to gain insights into complex data and make effective decisions. In the context of radiogenomics analysis with respect to clinical data, VA approaches offer promising directions in tumor profiling. However, VA approaches that bridge radiogenomic and clinical data in an interactive and flexible visual framework have not been investigated before. In this work, we enable the integrated exploration and analysis of radiogenomic data and clinical information for knowledge discovery and hypothesis assessment in a large cohort of prostate cancer patients. We handle missingness in the data through imputation techniques and apply unsupervised machine learning for the dimensionality reduction and clustering of the data to facilitate data handling and visualization. As a result, we present an interactive visual interface for two target audiences: cancer experts and biomedical data scientists. Our framework enables cancer experts to gain insights into the data by revealing new patterns or correlations in the datasets. It allows them to interactively assess and refine any hypothesis in mind for the underlying datasets. For biomedical data scientists, our framework offers the possibility to understand the analysis components and interactively explore their impact on the outcome. We evaluate the unsupervised machine learning models through similarity measures such as the silhouette coefficient. To assess the usability of the framework, we perform usage scenarios that we confirm by our cancer experts. The feedback from our domain experts reveals that our framework is a suitable and flexible technique to gain insights into large and heterogenous radiogenomic data with respect to clinical data. It promotes knowledge discovery as well as hypothesis creation, assessment, and refinement. Interacting with the different visualization and analysis components enhances the understanding of the data and the resulting visual representations. Our approach incorporates the integration of interactive visualization and automated analysis components. It supports our collaborating domain experts at the Medical University of Vienna to obtain new insights into their data, while investigating hypotheses at hand.",
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"title": "Advanced Importance Sampling Techniques for Virtual Ray Lights",
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"abstract": "This thesis provides new importance sampling techniques for Virtual Ray Lights forRendering Scenes in Participating Media. We will discuss the foundations of rendering scenes with participating media first, to get an understanding of this topic. Furthermore, we will provide an overview of different approaches that can be used for the rendering of these scenes. Virtual Ray Lights is an algorithm that traces light rays from the light source through the scene, which are then evaluated individually for each camera ray. Importance sampling is used on both rays to get a samples for each ray for which their contribution can be calculated. As a solid understanding of the original algorithm is needed to understand the newapproaches that we introduce, a focus is laid on explaining the mathematical foundations of the approach. We highlight the shortcomings that we found for rendering anisotropic participating media and introduce our solutions to solve them more efficiently. We provide two different solutions to the problem that we evaluated in the originalalgorithm. Our solutions are explained mathematically, via pseudo code and are evaluated with a multitude of tests. The goal of this thesis is to provide new, simple, robust and fast solutions to rendering scenes with anisotropic participating media.",
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"title": "Planarizing Graphs and Their Drawings by Vertex Splitting",
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"abstract": "The splitting number of a graph G= (V, E) is the minimum number of vertex splits required to turn G into a planar graph, where a vertex split removes a vertex v∈ V, introduces two new vertices v1, v2, and distributes the edges formerly incident to v among v1, v2. The splitting number problem is known to be NP-complete for abstract graphs and we provide a non-uniform fixed-parameter tractable (FPT) algorithm for this problem. We then shift focus to the splitting number of a given topological graph drawing in R2, where the new vertices resulting from vertex splits must be re-embedded into the existing drawing of the remaining graph. We show NP-completeness of this embedded splitting number problem, even for its two subproblems of (1) selecting a minimum subset of vertices to split and (2) for re-embedding a minimum number of copies of a given set of vertices. For the latter problem we present an FPT algorithm parameterized by the number of vertex splits. This algorithm reduces to a bounded outerplanarity case and uses an intricate dynamic program on a sphere-cut decomposition.",
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"title": "Quantitative Evaluation of Reading Times and Error Rates When Interpreting Visual Content",
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"abstract": "As static visualizations like diagrams, maps, charts, or drawings get more and more important in everyday life, it is crucial to find out how helpful they actually are, especially in comparison to text. This diploma thesis outlines if visualizations or texts are processed faster by humans and which representation is better comprehensible. For this purpose, we conducted an exploratory study in which we measured processing times and error rates when interpreting either texts or visualizations. In a pre-study, in which each participant had to describe four visualizations in their own words, we found out which parts of a visualization are deemed most important by people. The focus of the participants was on extrema, as well as on certain other values. Furthermore, the participants often compared different values in order to describe the visualization. The pre-study gave us valuable insights which we used for writing the texts for our study. For our exploratory study, where we wanted to find out more about whether visualizations or text can be interpreted more easily, we used 15 visualizations and texts that contained the same information. Each participant had to work on at least six topics, thereof at least three by using visualizations and three by using texts, and had to answer three questions per topic at the end. The time was measured while the participants worked on the topics.We could see that the participants solved topics by using visualizations on average about 1.3 times faster as compared to when they used texts. This difference was statistically significant. We could not find significant differences between the error rates for topics, when participants used visualizations or texts. When texts were used, we found correlations between text lengths and processing speeds, as well as text lengths and error rates. The content of visualizations or texts did not seem to play a role for processing speed or error rates. However, we found cases in which topics with visualizations were solved more than 50 % faster as compared to topics with texts. Our results provide a solid basis for defining further hypotheses regarding the readability of visualizations compared to text. In this thesis, we present the final hypotheses that emerge from our exploratory study. We consider these to be extremely interesting for visualization research, as there is much evidence that visual information can be processed faster than text. Whereby it is worth mentioning that the actual increase in performance may be much lower than often claimed in the media (e.g. `60,000 times faster than text'). Furthermore, our results indicate that no significant differences can be found between visual information and text with regard to the error rate in answering final questions.",
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"title": "Sampling-Distribution-Based Evaluation for Monte Carlo Rendering",
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"abstract": "In this paper, we investigate the application of per-pixel difference metrics for evaluating Monte Carlo (MC) rendering techniques. In particular, we propose to take the sampling distribution of the mean (SDM) into account for this purpose. We establish the theoretical background and analyze other per-pixel difference metrics, such as the absolute deviation (AD) and the mean squared error (MSE) in relation to the SDM. Based on insights from this analysis, we propose a new, alternative, and particularly easy-to-use approach, which builds on the SDM and facilitates meaningful comparisons of MC rendering techniques on a per-pixel basis. In order to demonstrate the usefulness of our approach, we compare it to commonly used metrics based on a variety of images computed with different rendering techniques. Our evaluation reveals limitations of commonly used metrics, in particular regarding the detection of differences between renderings that might be difficult to detect otherwise—this circ umstance is particularly apparent in comparison to the MSE calculated for each pixel. Our results indicate the potential of SDM-based approaches to reveal differences between MC renderers that might be caused by conceptual or implementation-related issues. Thus, we understand our approach as a way to facilitate the development and evaluation of rendering techniques.",
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"abstract": "Radiotherapy (RT) is one of the major curative approaches for cancer. It is a complex andrisky treatment approach, which requires precise planning, prior to the administrationof the treatment. Visual Computing (VC) is a fundamental component of RT planning,providing solutions in all parts of the process — from imaging to delivery.VC employs elements from computer graphics and image processing to create meaningful,interactive visual representations of medical data, and it has become an influentialfield of research for many advanced applications like radiation oncology. InteractiveVC approaches represent a new opportunity to integrate knowledgeable experts andtheir cognitive abilities in exploratory processes, which cannot be conducted by solelyautomatized methods.Despite the significant technological advancements of RT over the last decades, thereare still many challenges to address. In RT planning medical doctors need to consider avariety of information sources for anatomical and functional target volume delineation.The validation and inspection of the defined target volumes and the resulting RT planis a complex task, especially in the presence of moving target areas as it is the case fortumors of the chest and the upper abdomen, for instance, caused by breathing motion.Handling RT planning and delivery-related uncertainties, especially in the presence oftumor motion, is essential to improve the efficiency of the treatment and the minimizationof side effects.This dissertation contributes to the handling of RT planning related uncertainties byproposing novel VC methods. Quantification and visualization of these types of uncer-tainties will be an essential part of the presented methods, and aims at improving the RTworkflow in terms of delineation and registration accuracy, margin definitions and theinfluence of these uncertainties onto the dosimetric outcome. The publications presentedin this thesis address key aspects of the RT treatment planning process, where humaninteraction is required, and VC has the potential to improve the treatment outcome.First, major requirements for a multi-modal visualization framework are defined andimplemented with the aim to improve motion management by including 4D imageinformation. The visualization framework was designed to provide medical doctors withthe necessary visual information to improve the accuracy of tumor target delineationsand the efficiency of RT plan evaluation.xiii\nFurthermore, the topic of deformable image registration (DIR) accuracy is addressed inthis thesis. DIR has the potential to improve modern RT in many aspects, includingvolume definition, treatment planning, and image-guided adaptive RT. However, mea-suring DIR accuracy is difficult without known ground truth, but necessary before theintegration in the RT workflow. Visual assessment is an important step towards clinicalacceptance. A visualization framework is proposed, which supports the exploration andthe assessment of DIR accuracy. It offers different interaction and visualization featuresfor exploration of candidate regions to simplify the process of visual assessment, andthereby improve and contribute to its adequate use in RT planning.Finally, the topic of healthy tissue sparing is addressed with a novel visualization approachto interactively explore RT plans, and identify regions of healthy tissue, which can bespared further without compromising the treatment goals defined for tumor targets. Forthis, overlap volumes of tumor targets and healthy organs are included in the RT planevaluation process, and the initial visualization framework is extended with quantitativeviews. This enables quantitative properties of the overlap volumes to be interactivelyexplored, to identify critical regions and to steer the visualization for a detailed inspectionof candidates.All approaches were evaluated in user studies covering the individual visualizations andtheir interactions regarding helpfulness, comprehensibility, intuitiveness, decision-makingand speed, and if available using ground truth data to prove their validity.",
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