Exploring 3D surface data often involves navigating complex menus, creating challenges for both experts and new users who may struggle with overloaded interfaces. To address this, we introduce VocalVis , an open-source prototype that leverages real-time voice processing to simplify interaction and analysis of 3D surface data. By capitalizing on advancements in natural language processing, VocalVis enables users to interact with digital content through voice commands, addressing challenges associated with traditional navigation methods. Our study involves both data scientist novices and domain experts, offering insights into how voice interaction can streamline data exploration. The findings demonstrate the potential of voice as a powerful supplementary tool for visually analyzing complex datasets. This innovative approach opens up new possibilities for intuitive, accessible data exploration, making it convenient for a broader range of users to engage with 3D scientific data.

Abstract Surface noise provides a geometric alternative to color for encoding scalar information on surfaces, yet its perceptual characteristics remain insufficiently understood. We present a systematic investigation of how variations in noise amplitude and frequency are perceived when applied to 3D surfaces. Across three online perceptual studies with 142 participants, we gathered similarity judgments for surface noise stimuli and modeled the resulting perceptual space using multi‐dimensional scaling (MDS). Our analysis shows that the amplitude–frequency perceptual space requires at least three dimensions for accurate reconstruction; however, the stimuli lie near a 2D manifold. Building on these findings, we derive locally perceptually uniform reparameterizations for both amplitude and frequency, improving the suitability of surface noise as a mapping for scalar data. These results provide perceptual guidance for the design of geometric encodings based on surface perturbation.

This note corrects errors in Figs. 12 and 13 and the description of the parametric function in the paper "Perceptually Uniform Construction of Illustrative Textures" published in IEEE Transactions on Visualization and Computer Graphics, Vol. 30, Issue 1, 2024.

The diagnosis of aortic diseases could be significantly enhanced with modern advances in model-based vessel visualization, objective parameter quantification, as well as information gained through numerical blood flow simulation. Most state-of-the-art methods, however, require heavy processing and are often split across various frameworks that require setting up complex workflows, making many clinical applications unrealistic and hindering research on large datasets. We present the AortaAnalyzer, a unified, end-to-end pipeline for processing computed-tomography angiography (CTA) of the aorta, integrating a state-of-the-art 3D segmentation network (Dice 0 . 95 ± 0 . 01 , HD95 5 . 25 ± 5 . 73 mm), interactive correction tools, automated surface extraction, robust centerline computation, inlet/outlet capping for numerical hemodynamics, and clinical metric quantification. All modules share a single GUI, use standard formats (nrrd, STL, OBJ, CSV), and propagate changes automatically, eliminating complex multi-tool workflows. We developed the framework in an iterative process based on evaluations with seven independent experts—two numerical hemodynamics researchers, two vessel visualization researchers, two cardiac surgeons, and one radiologist. The framework received high usefulness ratings and feature requests drove the addition of surface capping and extended metric measurements. To assess efficiency, we compared processing time against 3D Slicer and SimVascular. The AortaAnalyzer demonstrated increased robustness and required substantially less manual interaction and overall processing time. AortaAnalyzer supports both clinical assessment and research purposes by providing rapid visualization of the vessel morphology, reproducible diameter, volume, and landmark analysis, and accelerated pre-processing for blood-flow simulation. It is open access and serves as an extendable platform.

The morphology of saccular intracranial aneurysms is widely assumed to encode biomechanical information critical for rupture risk, yet existing predictors rely almost exclusively on handcrafted shape descriptors that are subjective and capture only limited aspects of the geometry. We introduce a data-driven framework for uniform parametric mapping and statistical shape analysis of aneurysm surfaces, applied to a multi-institutional set of 958 patient-extracted intracranial aneurysms. We develop a uniform parametric mapping based on an angular- and radial-field parameterization, yielding a surface map into a registered canonical space. The mapping is fully automatic, requires no manual annotations, and is independent of the cut configuration, meshing regularity, and sampling density. Using the mapping, we create uniform point correspondences across all aneurysms. From these correspondences, high-dimensional shape vectors are constructed, and principal component analysis (PCA) yields the mean aneurysm shape and dominant modes of variation. We investigate how strongly each mode contributes to the rupture association using logistic regression of the PCA coefficients. We identified three modes significantly associated with rupture status ( ) and provide interpretable deformation patterns for their morphological characteristics. The shape-based regression model classifying rupture status achieves an average accuracy of and an AUC of after 5-fold cross-validation. Based on a diverse range of aneurysm models, our framework demonstrates robust parameter mapping and statistical measures, offering a generalized, reproducible approach for shape-based risk stratification that may inform evidence-based management of unruptured aneurysms.

Using speech for hands-free interaction in Virtual Reality (VR) is gaining popularity, supported by advances in natural language processing that enable accurate speech-to-text transcription and intent inference. Prior work has shown that voice-based navigation is effective when hand use is restricted or occupied, particularly when visually salient points in the scene, known as landmarks, can be identified and articulated. However, in many virtual environments, such landmarks may be absent or not easily recognizable, limiting the applicability of voice commands. To address this, we propose three landmark-free, user-centered coordinate systems to support speech-based locomotion in VR: Cartesian, cylindrical, and spherical. Each system uses a distinct encoding and interaction style. The Cartesian system employs a simple three-digit code and requires low cognitive effort. The spherical system prioritizes precision but demands higher mental effort. The cylindrical system combines elements of both, offering a balance between usability and accuracy. We evaluated these systems in a quantitative user study with 24 participants, all with backgrounds in information technology. The study assessed the systems’ effectiveness for object positioning and navigation, comparing them to teleportation, the standard non-voice locomotion method in VR. Results show that the Cartesian system enables faster and more intuitive navigation compared to the spherical system. Distinct usage patterns were observed, with users predominantly focusing on the central field of view during navigation. Our findings indicate that user-centered coordinate systems are practical to implement and present a viable alternative for speech-driven, hands-free navigation in VR.

lspr! ( lspr! ) is a powerful tool in clinical diagnostics and environmental monitoring for detecting various types of molecules. Building on this foundation, lspri! ( lspri! ) offers spatially-resolved sensing and has emerged as an active area of research with growing interest within the scientific community. However, analyzing lspri! data remains complex, requiring users to configure models, choose analysis parameters, and interpret derived metrics—often across disconnected tools or custom scripts. We present a visualization framework that supports users throughout the full analysis process. It automates certain aspects of the analysis while still allowing users to configure models and parameters and visualizes both intermediate and final results to facilitate comparison and interpretation. Our system was developed in close collaboration with domain experts through an iterative design process and evaluated through interviews with scientists using lspri! in their research. It has the potential to streamline lspri! data analysis, enabling researchers to explore, compare, and refine their modeling choices more efficiently.

Abstract Effective communication of climate science is critical as climate‐related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision‐making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text‐image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency. Sample analysis (n = 622) displayed equivalent retention by viewers between both formats. Metrics assessing interactivity found no correlation between interactivity and information retention. However, a stark contrast was observed in the time viewers spent engaging with each format. The video format was 29% more efficient with information taught over a period of time vs. the article. Additionally, retention on the video format worsened with age (P = 0.004) while retention on the article format improved with education (P = 0.038). These results align with previous findings in literature.

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Abstract In data science, visual data exploration becomes increasingly more challenging due to the continued rapid increase of data dimensionality and data sizes. To manage complexity, two orthogonal approaches are commonly used in practice: First, data is frequently clustered in high‐dimensional space by fitting mixture models composed of normal distributions or Student t‐distributions. Second, dimensionality reduction is employed to embed high‐dimensional point clouds in a two‐ or three‐dimensional space. Those algorithms determine the spatial arrangement in low‐dimensional space without further user interaction. This leaves little room for a guided exploration and data analysis. In this paper, we propose a novel visualization system for the effective exploration and construction of potential subspaces onto which mixture models can be projected. The subspaces are spanned linearly via basis vectors, for which a vast number of basis vector combinations is theoretically imaginable. Our system guides the user step‐by‐step through the selection process by letting users choose one basis vector at a time. To guide the process, multiple choices are pre‐visualized at once on a multi‐faceted prism. In addition to the qualitative visualization of the distributions, multiple quantitative metrics are calculated by which subspaces can be compared and reordered, including variance, sparsity, and visibility. Further, a bookmarking tool lets users record and compare different basis vector combinations. The usability of the system is evaluated by data scientists and is tested on several high‐dimensional data sets.

Intracranial aneurysms (IAs) are a critical public health concern: they are asymptomatic and can lead to fatal subarachnoid hemorrhage in case of rupture. Neuroradiologists rely on advanced imaging techniques to identify aneurysms in a patient and consider the characteristics of IAs along with several other patient-related factors for rupture risk assessment and treatment decision-making. The process of diagnostic image reading is time-intensive and prone to inter- and intra-individual variations, so researchers have proposed many computer-aided diagnosis (CAD) systems for aneurysm detection and segmentation. This paper provides a comprehensive literature survey of semi-automated and automated approaches for IA detection and segmentation and proposes a taxonomy to classify the approaches. We also discuss the current issues and give some insight into the future direction of CAD systems for IA detection and segmentation.

Uncertainty is an intrinsic property of almost all data, regardless of the data being measured, simulated, or generated. It can significantly influence the results and reliability of subsequent analysis steps. Clearly communicating uncertainties is crucial for informed decision-making and understanding, especially in biomolecular data, where uncertainty is often difficult to infer. Uncertainty visualization (UV) is a powerful tool for this purpose. However, previously proposed uncertainty visualization (UV) methods lack sufficient empirical evaluation. We collected and categorized visualization methods for portraying positional uncertainty in biomolecular structures. We then organized the methods into metaphorical groups and extracted nine representatives: color, clouds, ensemble, hulls, sausages, contours, texture, waves, and noise. We assessed their strengths and weaknesses in a twofold approach: expert assessments with six domain experts and three perceptual evaluations involving 1,756 participants. Through the expert assessments, we aimed to highlight the advantages and limitations of the individual methods for the application domain and discussed areas for necessary improvements. Through the perceptual evaluation, we investigated whether the visualizations are intuitively associated with uncertainty and whether the directionality of the mapping is perceived as intended. We also assessed the accuracy of inferring uncertainty values from the visualizations. Based on our results, we judged the appropriateness of the metaphors for encoding uncertainty and suggest further areas for improvement.

Generating visualizations from natural language queries is a useful extension to visualization libraries such as Vega-Lite. The goal of the NL2VIS task is to generate a valid Vega-Lite specification from a data frame and a natural language query as input, which can then be rendered as a visualization. To enable real-time interaction with the data, small model sizes and fast inferences are required. Previous work has introduced custom neural network solutions with custom visualization specifications and has not systematically tested pre-trained LMs to solve this problem. In this work, we opt for a more generic approach that (i) evaluates pre-trained LMs of different sizes and (ii) uses string encodings of data frames and visualization specifications instead of custom specifications. In our experiments, we show that these representations, in combination with pre-trained LMs, scale better than current state-of-the-art models. In addition, the small and base versions of the T5 architecture achieve real-time interaction, while LLMs far exceed latency thresholds suitable for visual exploration tasks. In summary, our models generate visualization specifications in real-time on a CPU and establish a new state of the art on the NL2VIS benchmark nvBench.

In various situations, such as clinical environments with sterile conditions or when hands are occupied with multiple devices, traditional methods of navigation and scene adjustment are impractical or even impossible. We explore a new solution by using voice control to facilitate interaction in virtual worlds to avoid the use of additional controllers. Therefore, we investigate three scenarios: Object Orientation, Visualization Customization, and Analytical Tasks and evaluate whether natural language interaction is possible and promising in each of these scenarios. In our quantitative user study participants were able to control virtual environments effortlessly using verbal instructions. This resulted in rapid orientation adjustments, adaptive visual aids, and accurate data analysis. In addition, user satisfaction and usability surveys showed consistently high levels of acceptance and ease of use. In conclusion, our study shows that the use of natural language can be a promising alternative for the improvement of user interaction in virtual environments. It enables intuitive interactions in virtual spaces, especially in situations where traditional controls have limitations. • Presenting an alternative, hands-free interaction system using real-time natural language processing. • Evaluation of VUIs in VR, spanning three primary categories: Orientation, Customization, and Analysis. • Exploring whether voice interaction can serve as a feasible mode of communication with visualizations. • Offering an open-source project to enhance accessibility for voice-based applications.

Science communication aims at making key research insights accessible to the broad public. If explanatory and exploratory visualization techniques are combined to do so, the approach is also referred to as exploranation. In this context, the audience is usually not required to have domain expertise. However, we show that exploranation can not only support the communication between researchers and a broad audience, but also between researchers directly. With the goal of communicating an existing method for conducting causal inference on spatio-temporal conflict event data, we investigated how to perform exploranation for experts, i.e., expert exploranation. Based on application scenarios of the inference method, we developed three versions of an interactive visual story to explain the method to conflict researchers. We abstracted the corresponding design process and evaluated the stories both with experts who were unfamiliar with the explained method and experts who were already familiar with it. The positive and extensive feedback from the evaluation shows that expert exploranation is a promising direction for visual storytelling, as it can help to improve scientific outreach, methodological understanding, and accessibility for researchers new to a field.

In this paper, we investigate the suitability of different visual representations of pathological growth and shrinkage using surface models of intracranial aneurysms and liver tumors. By presenting complex medical information in a visually accessible manner, audiences can better understand and comprehend the progression of pathological structures. Previous work in medical visualization provides an extensive design space for visualizing medical image data. However, determining which visualization techniques are appropriate for a general audience has not been thoroughly investigated. We conducted a user study (n = 40) to evaluate different visual representations in terms of their suitability for solving tasks and their aesthetics. We created surface models representing the evolution of pathological structures over multiple discrete time steps and visualized them using illumination-based and illustrative techniques. Our results indicate that users’ aesthetic preferences largely coincide with their preferred visualization technique for task-solving purposes. In general, the illumination-based technique has been preferred to the illustrative technique, but the latter offers great potential for increasing the accessibility of visualizations to users with color vision deficiencies.

Abstract The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. While these so‐called hemodynamic simulations offer groundbreaking opportunities to solve problems at the clinical forefront, a successful translation of CFD to clinical decision‐making is challenging. Hemodynamic simulations are intrinsically complex, time‐consuming, and resource‐intensive, which conflicts with the time‐sensitive nature of clinical workflows and the fact that hospitals usually do not have the necessary resources or infrastructure to support CFD simulations. To address these transfer challenges, we propose a novel visualization system which enables instant flow exploration without performing on‐site simulation. To gain insights into the viability of the approach, we focus on hemodynamic simulations of the carotid bifurcation, which is a highly relevant arterial subtree in stroke diagnostics and prevention. We created an initial database of 120 high‐resolution carotid bifurcation flow models and developed a set of similarity metrics used to place a new carotid surface model into a neighborhood of simulated cases with the highest geometric similarity. The neighborhood can be immediately explored and the flow fields analyzed. We found that if the artery models are similar enough in the regions of interest, a new simulation leads to coinciding results, allowing the user to circumvent individual flow simulations. We conclude that similarity‐based visual analysis is a promising approach toward the usability of CFD in medical practice.

Abstract Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response to this, we propose a novel technique, InverseVis, which leverages curved sphere tracing and uses the otherwise unused space to enhance visibility. Our approach combines direct and indirect rendering, allowing camera rays to wrap around the surface and reveal information from the backside. To achieve this, we formulate an energy term that guides the image synthesis in previously unused space, highlighting the most important regions of the backside. By quantifying the amount of visible important features, we optimize the camera position to maximize the visibility of the scalar field on both the front and backsides. InverseVis is benchmarked against state‐of‐the‐art methods and a derived technique, showcasing its effectiveness in revealing essential features and outperforming existing approaches.

Abstract The smoothing of surface curves is an essential tool in mesh processing, important to applications that require segmenting and cutting surfaces such as surgical planning. Surface curves are typically designed by professionals to match certain surface features. For this reason, the smoothed curves should be close to the original and easily adjustable by the user in interactive tools. Previous methods achieve this desired behavior, e.g., by utilizing energy‐minimizing splines or generalizations of Bézier splines, which require a significant number of control points and may not provide interactive frame rates or numerical stability. This paper presents a new algorithm for robust smoothing of discrete surface curves on triangular surface meshes. By using a scalar penalty potential as the fourth coordinate, the given surface mesh is embedded into the 4D Euclidean space. Our method is based on finding geodesics in this lifted surface, which are then projected back onto the original 3D surface. The benefits of this approach include guaranteed convergence and good approximation of the initial curve. We propose a family of penalty potentials with one single parameter for adjusting the trade‐off between smoothness and similarity. The implementation of our method is straightforward as we rely on existing methods for computing geodesics and penalty fields. We evaluate our implementation and confirm its robustness and efficiency.

Line attributes such as width and dashing are commonly used to encode information. However, many questions on the perception of line attributes remain, such as how many levels of attribute variation can be distinguished or which line attributes are the preferred choices for which tasks. We conducted three studies to develop guidelines for using stylized lines to encode scalar data. In our first study, participants drew stylized lines to encode uncertainty information. Uncertainty is usually visualized alongside other data. Therefore, alternative visual channels are important for the visualization of uncertainty. Additionally, uncertainty-e.g., in weather forecasts-is a familiar topic to most people. Thus, we picked it for our visualization scenarios in study 1. We used the results of our study to determine the most common line attributes for drawing uncertainty: Dashing, luminance, wave amplitude, and width. While those line attributes were especially common for drawing uncertainty, they are also commonly used in other areas. In studies 2 and 3, we investigated the discriminability of the line attributes determined in study 1. Studies 2 and 3 did not require specific application areas; thus, their results apply to visualizing any scalar data in line attributes. We evaluated the just-noticeable differences (JND) and derived recommendations for perceptually distinct line levels. We found that participants could discriminate considerably more levels for the line attribute width than for wave amplitude, dashing, or luminance.

Illustrative textures, such as stippling or hatching, were predominantly used as an alternative to conventional Phong rendering. Recently, the potential of encoding information on surfaces or maps using different densities has also been recognized. This has the significant advantage that additional color can be used as another visual channel and the illustrative textures can then be overlaid. Effectively, it is thus possible to display multiple information, such as two different scalar fields on surfaces simultaneously. In previous work, these textures were manually generated and the choice of density was unempirically determined. Here, we first want to determine and understand the perceptual space of illustrative textures. We chose a succession of simplices with increasing dimensions as primitives for our textures: Dots, lines, and triangles. Thus, we explore the texture types of stippling, hatching, and triangles. We create a range of textures by sampling the density space uniformly. Then, we conduct three perceptual studies in which the participants performed pairwise comparisons for each texture type. We use multidimensional scaling (MDS) to analyze the perceptual spaces per category. The perception of stippling and triangles seems relatively similar. Both are adequately described by a 1D manifold in 2D space. The perceptual space of hatching consists of two main clusters: Crosshatched textures, and textures with only one hatching direction. However, the perception of hatching textures with only one hatching direction is similar to the perception of stippling and triangles. Based on our findings, we construct perceptually uniform illustrative textures. Afterwards, we provide concrete application examples for the constructed textures.

Gaussian mixture models are classical but still popular machine learning models. An appealing feature of Gaussian mixture models is their tractability, that is, they can be learned efficiently and exactly from data, and also support efficient exact inference queries like soft clustering data points. Only seemingly simple, Gaussian mixture models can be hard to understand. There are at least four aspects to understanding Gaussian mixture models, namely, understanding the whole distribution, its individual parts (mixture components), the relationships between the parts, and the interplay of the whole and its parts. In a structured literature review of applications of Gaussian mixture models, we found the need for supporting all four aspects. To identify candidate visualizations that effectively aid the user needs, we structure the available design space along three different representations of Gaussian mixture models, namely as functions, sets of parameters, and sampling processes. From the design space, we implemented three design concepts that visualize the overall distribution together with its components. Finally, we assessed the practical usefulness of the design concepts with respect to the different user needs in expert interviews and an insight-based user study.

As locomotion is an important factor in improving Virtual Reality (VR) immersion and usability, research in this area has been and continues to be a crucial aspect for the success of VR applications. In recent years, a variety of techniques have been developed and evaluated, ranging from abstract control, vehicle, and teleportation techniques to more realistic techniques such as motion, gestures, and gaze. However, when it comes to hands-free scenarios, for example to increase the overall accessibility of an application or in medical scenarios under sterile conditions, most of the announced techniques cannot be applied. This is where the use of speech as an intuitive means of navigation comes in handy. As systems become more capable of understanding and producing speech, voice interfaces become a valuable alternative for input on all types of devices. This takes the quality of hands-free interaction to a new level. However, intuitive user-assisted speech interaction is difficult to realize due to semantic ambiguities in natural language utterances as well as the high real-time requirements of these systems. In this paper, we investigate steering-based locomotion and selection-based locomotion using three speech-based, hands-free methods and compare them with leaning as an established alternative. Our results show that landmark-based locomotion is a convenient, fast, and intuitive way to move between locations in a VR scene. Furthermore, we show that in scenarios where landmarks are not available, number grid-based navigation is a successful solution. Based on this, we conclude that speech is a suitable alternative in hands-free scenar-ios, and exciting ideas are emerging for future work focused on developing hands-free ad hoc navigation systems for scenes where landmarks do not exist or are difficult to articulate or recognize.

Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user’s trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data.

Abstract Analyzing stenoses of the internal carotids – local constrictions of the artery – is a critical clinical task in cardiovascular disease treatment and prevention. For this purpose, we propose a self‐contained pipeline for the visual analysis of carotid artery geometries. The only inputs are computed tomography angiography (CTA) scans, which are already recorded in clinical routine. We show how integrated model extraction and visualization can help to efficiently detect stenoses and we provide means for automatic, highly accurate stenosis degree computation. We directly connect multiple sophisticated processing stages, including a neural prediction network for lumen and plaque segmentation and automatic global diameter computation. We enable interactive and retrospective user control over the processing stages. Our aims are to increase user trust by making the underlying data validatable on the fly, to decrease adoption costs by minimizing external dependencies, and to optimize scalability by streamlining the data processing. We use interactive visualizations for data inspection and adaption to guide the user through the processing stages. The framework was developed and evaluated in close collaboration with radiologists and neurologists. It has been used to extract and analyze over 100 carotid bifurcation geometries and is built with a modular architecture, available as an extendable open‐source platform.

Presenting medical volume data using augmented reality (AR) can facilitate the identification of anatomical structures, the perception of their spatial relations and the development of mental maps compared to more commonly used monitors. However, interaction methods explored in these conventional settings may not be applicable in AR environments, or perform differently. In terms of mode activation, gestural interaction was shown to be a viable, touchless alternative to traditional input devices, which is desirable in sterile medical use cases. Therefore, we present a user study (n = 21) comparing hand and foot gestures with voice commands for the activation of interaction modes within a projector-based, spatial AR prototype to visualize medical volume data. Interaction itself was performed via hand movements captured by a data glove. Consistent, statistically significant results across measured variables suggest advantages of voice commands. In addition, a second experiment (n = 17) compared the hand-based interaction with two motion-sensitive devices held in power and in precision grip respectively. All modes were activated using voice commands. No considerable differences between tested grip styles could be determined. The findings suggest that the choice of preferable interaction devices is user and use case dependent.

The Gaussian mixture model (GMM) describes the distribution of random variables from several different populations. GMMs have widespread applications in probability theory, statistics, machine learning for unsupervised cluster analysis and topic modeling, as well as in deep learning pipelines. So far, few efforts have been made to explore the underlying point distribution in combination with the GMMs, in particular when the data becomes high-dimensional and when the GMMs are composed of many Gaussians. We present an analysis tool comprising various GPU-based visualization techniques to explore such complex GMMs. To facilitate the exploration of high-dimensional data, we provide a novel navigation system to analyze the underlying data. Instead of projecting the data to 2D, we utilize interactive 3D views to better support users in understanding the spatial arrangements of the Gaussian distributions. The interactive system is composed of two parts: (1) raycasting-based views that visualize cluster memberships, spatial arrangements, and support the discovery of new modes. (2) overview visualizations that enable the comparison of Gaussians with each other, as well as small multiples of different choices of basis vectors. Users are supported in their exploration with customization tools and smooth camera navigations. Our tool was developed and assessed by five domain experts, and its usefulness was evaluated with 23 participants. To demonstrate the effectiveness, we identify interesting features in several data sets.

We present the framework GUCCI (Guided Cardiac Cohort Investigation), which provides a guided visual analytics workflow to analyze cohort-based measured blood flow data in the aorta. In the past, many specialized techniques have been developed for the visual exploration of such data sets for a better understanding of the influence of morphological and hemodynamic conditions on cardiovascular diseases. However, there is a lack of dedicated techniques that allow visual comparison of multiple data sets and defined cohorts, which is essential to characterize pathologies. GUCCI offers visual analytics techniques and novel visualization methods to guide the user through the comparison of predefined cohorts, such as healthy volunteers and patients with a pathologically altered aorta. The combination of overview and glyph-based depictions together with statistical cohort-specific information allows investigating differences and similarities of the time-dependent data. Our framework was evaluated in a qualitative user study with three radiologists specialized in cardiac imaging and two experts in medical blood flow visualization. They were able to discover cohort-specific characteristics, which supports the derivation of standard values as well as the assessment of pathology-related severity and the need for treatment.

An accurate assessment of the cardiovascular system and prediction of cardiovascular diseases (CVDs) are crucial. Cardiac blood flow data provide insights about patient-specific hemodynamics. However, there is a lack of machine learning approaches for a feature-based classification of heart-healthy people and patients with CVDs. In this paper, we investigate the potential of morphological and hemodynamic features extracted from measured blood flow data in the aorta to classify heart-healthy volunteers (HHV) and patients with bicuspid aortic valve (BAV). Furthermore, we determine features that distinguish male vs. female patients and elderly HHV vs. BAV patients. We propose a data analysis pipeline for cardiac status classification, encompassing feature selection, model training, and hyperparameter tuning. Our results suggest substantial differences in flow features of the aorta between HHV and BAV patients. The excellent performance of the classifiers separating between elderly HHV and BAV patients indicates that aging is not associated with pathological morphology and hemodynamics. Our models represent a first step towards automated diagnosis of CVS using interpretable machine learning models.

The helical axis is a common tool used in biomechanical modeling to parameterize the motion of rigid objects. It encodes an object’s rotation around and translation along a unique axis. Visualizations of helical axes have helped to make kinematic data tangible. However, the analysis process often remains tedious, especially if complex motions are examined. We identify multiple key challenges: the absence of interactive tools for the computation and handling of helical axes, visual clutter in axis representations, and a lack of contextualization. We solve these issues by providing the first generalized framework for kinematic analysis with helical axes. Axis sets can be computed on-demand, interactively filtered, and explored in multiple coordinated views. We iteratively developed and evaluated the HAExplorer with active biomechanics researchers. Our results show that the techniques we introduce open up the possibility to analyze non-planar, compound, and interdependent motion data.

Henrik Voigt, Ozge Alacam, Monique Meuschke, Kai Lawonn, Sina Zarrieß. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Since for most consumers the Virtual Reality (VR) experience exceeds that of desktop applications, an increasing number of applications is being transferred from desktop to VR. Industrial and entertainment applications primarily expect for a richer consumer experience, while others, such as surgical applications, seek for improved precision over their desktop counterparts. One way to improve the performance of precision-based VR applications is to provide suitable visualizations. Today, these "suitable" visualizations are mostly transferred from desktop to VR without considering their spatial and temporal performance might change in VR. This may not lead to an optimal solution, which can be crucial for precision-based tasks. Misinterpretation of shape or distance in a surgical or pre-operative simulation can affect the chosen treatment and thus directly impact the outcome. Therefore, we evaluate the performance differences of multiple visualizations for 3D surfaces based on their shape and distance estimation for desktop and VR applications. We conducted a quantitative user study with 56 participants evaluating seven visualizations (Phong, Toon, Fresnel, Pseudo-Chromadepth, Heatmap, Isolines, and Arrow Glyphs). Our results show that the performance of each visualization varies depending on the task, system, and surface type, with VR generally providing improved results. While Isolines are able to improve distance estimation, Phong and Heatmaps are beneficial for shape estimation.

In multiple sclerosis (MS), the amount of brain damage, anatomical location, shape, and changes are important aspects that help medical researchers and clinicians to understand the temporal patterns of the disease. Interactive visualization for longitudinal MS data can support studies aimed at exploratory analysis of lesion and healthy tissue topology. Existing visualizations in this context comprise bar charts and summary measures, such as absolute numbers and volumes to summarize lesion trajectories over time, as well as summary measures such as volume changes. These techniques can work well for datasets having dual time point comparisons. For frequent follow-up scans, understanding patterns from multimodal data is difficult without suitable visualization approaches. As a solution, we propose a visualization application, wherein we present lesion exploration tools through interactive visualizations that are suitable for large time-series data. In addition to various volumetric and temporal exploration facilities, we include an interactive stacked area graph with other integrated features that enable comparison of lesion features, such as intensity or volume change. We derive the input data for the longitudinal visualizations from automated lesion tracking. For cases with a larger number of follow-ups, our visualization design can provide useful summary information while allowing medical researchers and clinicians to study features at lower granularities. We demonstrate the utility of our visualization on simulated datasets through an evaluation with domain experts.

Abstract Map‐like visualizations of patient‐specific cardiovascular structures have been applied in numerous medical application contexts. The term map‐like alludes to the characteristics these depictions share with cartographic maps: they show the spatial relations of data attributes from a single perspective, they abstract the underlying data to inCrease legibility, and they facilitate tasks centered around overview, navigation, and comparison. A vast landscape of techniques exists to derive such maps from heterogeneous data spaces. Yet, they all target similar purposes within disease diagnostics, treatment, or research and they face coinciding challenges in mapping the spatial component of a treelike structure to a legible layout. In this report, we present a framing to unify these approaches. On the one hand, we provide a classification of the existing literature according to the data spaces such maps can be derived from. On the other hand, we view the approaches in light of the manifold requirements medical practitioners and researchers have in their efforts to combat the ever‐growing burden of cardiovascular disease. Based on these two perspectives, we offer recommendations for the design of map‐like visualizations of the cardiovascular system.

Abstract In this work, we investigate how concepts from medical flow visualization can be applied to enhance stroke prevention diagnostics. Our focus lies on carotid stenoses, i.e., local narrowings of the major brain‐supplying arteries, which are a frequent cause of stroke. Carotid surgery can reduce the stroke risk associated with stenoses, however, the procedure entails risks itself. Therefore, a thorough assessment of each case is necessary. In routine diagnostics, the morphology and hemodynamics of an afflicted vessel are separately analyzed using angiography and sonography, respectively. Blood flow simulations based on computational fluid dynamics could enable the visual integration of hemodynamic and morphological information and provide a higher resolution on relevant parameters. We identify and abstract the tasks involved in the assessment of stenoses and investigate how clinicians could derive relevant insights from carotid blood flow simulations. We adapt and refine a combination of techniques to facilitate this purpose, integrating spatiotemporal navigation, dimensional reduction, and contextual embedding. We evaluated and discussed our approach with an interdisciplinary group of medical practitioners, fluid simulation and flow visualization researchers. Our initial findings indicate that visualization techniques could promote usage of carotid blood flow simulations in practice.

Abstract The exploration of large‐scale conflicts, as well as their causes and effects, is an important aspect of socio‐political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi‐automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user‐defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developedVEHICLE, a web‐based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map‐based depictions as well as advanced interaction techniques. We showed the usefulness ofVEHICLEin two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.

We propose a visualization application, designed for the exploration of human spine simulation data. Our goal is to support research in biomechanical spine simulation and advance efforts to implement simulation-backed analysis in surgical applications. Biomechanical simulation is a state-of-the-art technique for analyzing load distributions of spinal structures. Through the inclusion of patient-specific data, such simulations may facilitate personalized treatment and customized surgical interventions. Difficulties in spine modelling and simulation can be partly attributed to poor result representation, which may also be a hindrance when introducing such techniques into a clinical environment. Comparisons of measurements across multiple similar anatomical structures and the integration of temporal data make commonly available diagrams and charts insufficient for an intuitive and systematic display of results. Therefore, we facilitate methods such as multiple coordinated views, abstraction and focus and context to display simulation outcomes in a dedicated tool. By linking the result data with patient-specific anatomy, we make relevant parameters tangible for clinicians. Furthermore, we introduce new concepts to show the directions of impact force vectors, which were not accessible before. We integrated our toolset into a spine segmentation and simulation pipeline and evaluated our methods with both surgeons and biomechanical researchers. When comparing our methods against standard representations that are currently in use, we found increases in accuracy and speed in data exploration tasks. in a qualitative review, domain experts deemed the tool highly useful when dealing with simulation result data, which typically combines time-dependent patient movement and the resulting force distributions on spinal structures.

Abstract The study of vascular structures, using medical 3D models, is an active field of research. Illustrative visualizations have been applied to this domain in multiple ways. Researchers made the geometric properties of vasculature more comprehensive and augmented the surface with representations of multivariate clinical data. Techniques that head beyond the application of colour‐maps or simple shading approaches require a surface parameterization, that is, texture coordinates, in order to overcome locality. When extracting 3D models, the computation of texture coordinates on the mesh is not always part of the data processing pipeline. We combine existing techniques to a simple parameterization approach that is suitable for tree‐like structures. The parameterization is done w.r.t. to a pre‐defined source vertex. For this, we present an automatic algorithm, that detects the tree root. The parameterization is partly done in screen‐space and recomputed per frame. However, the screen‐space computation comes with positive features that are not present in object‐space approaches. We show how the resulting texture coordinates can be used for varying hatching, contour parameterization, display of decals, as additional depth cues and feature extraction. A further post‐processing step based on parameterization allows for a segmentation of the structure and visualization of its tree topology.

Abstract We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.

The mitral valve, one of the four valves in the human heart, controls the bloodflow between the left atrium and ventricle and may suffer from various pathologies. Malfunctioning valves can be treated by reconstructive surgeries, which have to be carefully planned and evaluated. While current research focuses on the modeling and segmentation of the valve, we base our work on existing segmentations of patient-specific mitral valves, that are also time-resolved ( 3D+t) over the cardiac cycle. The interpretation of the data can be ambiguous, due to the complex surface of the valve and multiple time steps. We therefore propose a software prototype to analyze such 3D+t data, by extracting pathophysiological parameters and presenting them via dimensionally reduced visualizations. For this, we rely on an existing algorithm to unroll the convoluted valve surface towards a flattened 2D representation. In this paper, we show that the 3D+t data can be transferred to 3D or 2D representations in a way that allows the domain expert to faithfully grasp important aspects of the cardiac cycle. In this course, we not only consider common pathophysiological parameters, but also introduce new observations that are derived from landmarks within the segmentation model. Our analysis techniques were developed in collaboration with domain experts and a survey showed that the insights have the potential to support mitral valve diagnosis and the comparison of the pre- and post-operative condition of a patient.

Augmented reality (AR) may be a useful technique to overcome issues of conventionally used navigation systems supporting medical needle insertions, like increased mental workload and complicated hand-eye coordination. Previous research primarily focused on the development of AR navigation systems designed for specific displaying devices, but differences between employed methods have not been investigated before. To this end, a user study involving a needle insertion task was conducted comparing different AR display techniques with a monitor-based approach as baseline condition for the visualization of navigation information. A video see-through stationary display, an optical see-through head-mounted display and a spatial AR projector-camera-system were investigated in this comparison. Results suggest advantages of using projected navigation information in terms of lower task completion time, lower angular deviation and affirmative subjective participant feedback. Techniques requiring the intermediate view on screens, i.e. the stationary display and the baseline condition, showed less favorable results. Thus, benefits of providing AR navigation information compared to a conventionally used method could be identified. Significant objective measures results, as well as an identification of advantages and disadvantages of individual display techniques contribute to the development and design of improved needle navigation systems.

Movie recommendation systems have become ubiquitous in most sides of our lives. Currently, they are far from optimal. This paper presents a movielense recommendation system based on machine learning through utilizing the deep convolutional network and depending on generative modeling of public previous aspects mixtures. The objective of this paper is to introduce such a recommendation system to help users in selecting datasets of movies according to certain pre-specified measurements and data. The applied methodology is pivoted on implementing the system by using different sentimental analysis algorithms. These algorithms are keen to provide a solution for the full stack developers through using a trained model using their datasets. This will give suggestions based on their previous activity or recommended by other users’ interests demonstrated on their website. Thus to help users visualize their interest or to form the better scope of visualization. The presented system has proved better results concerning accuracy and efficiency in comparison with some other similar works. When experimentations on both real and synthetic datasets were conducted, the system showed percentile improvement of about 91.07%in the training dataset and 93.49%in the testing dataset respectively. This system is convenient for several application fields like time series network visualization, business process modeling, various data mining applications, e-commerce websites, besides most online platforms that people use including social media.

Augmented reality (AR) is a promising tool to convey useful information at the place where it is needed. However, perceptual issues with augmented reality visualizations affect the estimation of distances and depth and thus can lead to critically wrong assumptions. These issues have been successfully investigated for video see-through modalities. Moreover, advanced visualization methods encoding depth information by displaying additional depth cues were developed. In this work, state-of-the-art visualization concepts were adopted for a projective AR setup. We conducted a user study to assess the concepts’ suitability to convey depth information. Participants were asked to sort virtual cubes by using the provided depth cues. The investigated visualization concepts consisted of conventional Phong shading, a virtual mirror, depth-encoding silhouettes, pseudo-chromadepth rendering and an illustrative visualization using supporting line depth cues. Besides different concepts, we altered between a monoscopic and a stereoscopic display mode to examine the effects of stereopsis. Consistent results across variables show a clear ranking of examined concepts. The supporting lines approach and the pseudo-chromadepth rendering performed best. Stereopsis was shown to provide significant advantages for depth perception, while the current visualization technique had only little effect on investigated measures in this condition. However, similar results were achieved using the supporting lines and the pseudo-chromadepth concepts in a monoscopic setup. Our study showed the suitability of advanced visualization concepts for the rendering of virtual content in projective AR. Specific depth estimation results contribute to the future design and development of applications for these systems.

Abstract We present an interactive approach to analyse flow fields using a new type of particle system, which is composed of autonomous particles exploring the flow. While particles provide a very intuitive way to visualize flows, it is a challenge to capture the important features with such systems. Particles tend to cluster in regions of low velocity and regions of interest are often sparsely populated. To overcome these disadvantages, we propose an automatic adaption of the particle density with respect to local importance measures. These measures are user defined and the systems sensitivity to them can be adjusted interactively. Together with the particle history, these measures define a probability for particles to multiply or die, respectively. There is no communication between the particles and no neighbourhood information has to be maintained. Thus, the particles can be handled in parallel and support a real‐time investigation of flow fields. To enhance the visualization, the particles' properties and selected field measures are also used to specify the systems rendering parameters, such as colour and size. We demonstrate the effectiveness of our approach on different simulated vector fields from technical and medical applications.

Abstract The art of representing images with triangles is known as image triangulation, which purposefully uses abstraction and simplification to guide the viewer's attention. The manual creation of image triangulations is tedious and thus several tools have been developed in the past that assist in the placement of vertices by means of image feature detection and subsequent Delaunay triangulation. In this paper, we formulate the image triangulation process as an optimization problem. We provide an interactive system that optimizes the vertex locations of an image triangulation to reduce the root mean squared approximation error. Along the way, the triangulation is incrementally refined by splitting triangles until certain refinement criteria are met. Thereby, the calculation of the energy gradients is expensive and thus we propose an efficient rasterization‐based GPU implementation. To ensure that artists have control over details, the system offers a number of direct and indirect editing tools that split, collapse and re‐triangulate selected parts of the image. For final display, we provide a set of rendering styles, including constant colours, linear gradients, tonal art maps and textures. Finally, we demonstrate temporal coherence for animations and compare our method with existing image triangulation tools.

Abstract We present a method for detecting and segmenting aneurysms in blood vessels that facilitates the assessment of risks associated with the aneurysms. The detection and analysis of aneurysms is important for medical diagnosis as aneurysms bear the risk of rupture with fatal consequences for the patient. For risk assessment and treatment planning, morphological descriptors, such as the height and width of the aneurysm, are used. Our system enables the fast detection, segmentation and analysis of single and multiple aneurysms. The method proceeds in two stages plus an optional third stage in which the user interacts with the system. First, a set of aneurysm candidate regions is created by segmenting regions of the vessels. Second, the aneurysms are detected by a classification of the candidates. The third stage allows users to adjust and correct the result of the previous stages using a brushing interface. When the segmentation of the aneurysm is complete, the corresponding ostium curves and morphological descriptors are computed and a report including the results of the analysis and renderings of the aneurysms is generated. The novelty of our approach lies in combining an analytic characterization of aneurysms and vessels to generate a list of candidate regions with a classifier trained on data to identify the aneurysms in the candidate list. The candidate generation is modeled as a global combinatorial optimization problem that is based on a local geometric characterization of aneurysms and vessels and can be efficiently solved using a graph cut algorithm. For the aneurysm classification scheme, we identified four suitable features and modeled appropriate training data. An important aspect of our approach is that the resulting system is fast enough to allow for user interaction with the global optimization by specifying additional constraints via a brushing interface.

Abstract Simulations and measurements of blood and airflow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis and treatment of diseases. This survey focuses on three main application areas. (1) Computational fluid dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D phase‐contrast (4D PC) magnetic resonance imaging of aortic haemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties and providing a more fine‐granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature‐based visualization, the visual comparison of different simulation results and the interactive visual analysis of the flow field and derived characteristics.

Augmented reality (AR) is a promising tool to improve instrument navigation in needle-based interventions. Limited research has been conducted regarding suitable navigation visualizations. In this work, three navigation concepts based on existing approaches were compared in a user study using a projective AR setup. Each concept was implemented with three different scales for accuracy-to-color mapping and two methods of navigation indicator scaling. Participants were asked to perform simulated needle insertion tasks with each of the resulting 18 prototypes. Insertion angle and insertion depth accuracies were measured and analyzed, as well as task completion time and participants' subjectively perceived task difficulty. Results show a clear ranking of visualization concepts across variables. Less consistent results were obtained for the color and indicator scaling factors. Results suggest that logarithmic indicator scaling achieved better accuracy, but participants perceived it to be more difficult than linear scaling. With specific results for angle and depth accuracy, our study contributes to the future composition of improved navigation support and systems for precise needle insertion or similar applications.

This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm's state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.

We present a Cerebral Aneurysm Vortex Classification (CAVOCLA) that allows to classify blood flow in cerebral aneurysms. Medical studies assume a strong relation between the progression and rupture of aneurysms and flow patterns. To understand how flow patterns impact the vessel morphology, they are manually classified according to predefined classes. However, manual classifications are time-consuming and exhibit a high inter-observer variability. In contrast, our approach is more objective and faster than manual methods. The classification of integral lines, representing steady or unsteady blood flow, is based on a mapping of the aneurysm surface to a hemisphere by calculating polar-based coordinates. The lines are clustered and for each cluster a representative is calculated. Then, the polar-based coordinates are transformed to the representative as basis for the classification. Classes are based on the flow complexity. The classification results are presented by a detail-on-demand approach using a visual transition from the representative over an enclosing surface to the associated lines. Based on seven representative datasets, we conduct an informal interview with five domain experts to evaluate the system. They confirmed that CAVOCLA allows for a robust classification of intra-aneurysmal flow patterns. The detail-on-demand visualization enables an efficient exploration and interpretation of flow patterns.

In many of today’s big data analytics applications, it might need to analyze social media feeds as well as to visualize users’ opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on Twitter and deemed to be within the confines of Data Mining. It is concerned with Natural Language Processing (NLP)-based sentiment analysis for Twitter’s opinion mining. As such, the objective of this work is to use a data mining approach of text-feature extraction, classification, and dimensionality reduction, using sentiment analysis to analyze and visualize Twitter users’ opinion. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to get word scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using BNN. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to extract meaningful data from tweets in real-time and store them into a relational model for analysis. And then use our dimension reduction method. This will help people discover the correlation of the leading role between them, which also reflects news media’s focuses and people’s interests. This system has proved better results with respect to accuracy and efficiency in comparison with some other similar works. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer’s behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.

Abstract Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential. Such fused overview visualizations guide physicians to suspicious regions to be analysed in detail, e.g. with slice‐based viewing. We give an overview of state of the art in multi‐modal medical data visualization techniques. Multi‐modal medical data consist of multiple scans of the same subject using various acquisition methods, often combining multiple complimentary types of information. Three‐dimensional visualization techniques for multi‐modal medical data can be used in diagnosis, treatment planning, doctor–patient communication as well as interdisciplinary communication. Over the years, multiple techniques have been developed in order to cope with the various associated challenges and present the relevant information from multiple sources in an insightful way. We present an overview of these techniques and analyse the specific challenges that arise in multi‐modal data visualization and how recent works aimed to solve these, often using smart visibility techniques. We provide a taxonomy of these multi‐modal visualization applications based on the modalities used and the visualization techniques employed. Additionally, we identify unsolved problems as potential future research directions.

Abstract In this paper, we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub‐area of visualization. For the remainder of the survey, we then focus on surface‐based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods' implementations. We then provide an overview of low‐level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.

Abstract The surface of a molecule holds important information about the interaction behavior with other molecules. In dynamic folding or docking processes, residues of amino acids with different properties change their position within the molecule over time. The atoms of the residues that are accessible to the solvent can directly contribute to binding interactions, while residues buried within the molecular structure contribute to the stability of the molecule. Understanding patterns and causality of structural changes is important for experts in the pharmaceutical domain, e.g., in the process of drug design. We apply an iterative computation of the Solvent Accessible Surface in order to extract virtual layers of a molecule. The extraction allows to track the movement of residues in the body of the molecule, with respect to the distance of the residue to the surface or the core during dynamics simulations. We visualize the obtained layer information for the complete time span of the molecular dynamics simulation as a 2D‐map and for individual time‐steps as a 3D‐representation of the molecule. The data acquisition has been implemented alongside with further analysis functionality in a prototypical application, which is available to the public domain. We underline the feasibility of our approach with a study from the pharmaceutical domain, where our approach has been used for novel insights into the folding behavior of μ‐conotoxins.

Abstract We present the first visualization tool that enables a comparative depiction of structural stress tensor data for vessel walls of cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of analyzing the interaction of morphological and hemodynamic information for the patient‐specific rupture risk evaluation and treatment analysis. Tensor data such as the stress inside the aneurysm walls characterizes the interplay between the morphology and blood flow and seems to be an important rupture‐prone criterion. We use different glyph‐based techniques to depict local stress tensors simultaneously and compare their applicability to cerebral aneurysms in a user study. We thus offer medical researchers an effective visual exploration tool to assess the aneurysm rupture risk. We developed a GPU‐based implementation of our techniques with a flexible interactive data exploration mechanism. Our depictions are designed in collaboration with domain experts, and we provide details about the evaluation.

We present novel techniques for visualizing, illustrating, analyzing, and generating carvings in surfaces. In particular, we consider the carvings in the plaster of the cloister of the Magdeburg cathedral, which dates to the 13th century. Due to aging and weathering, the carvings have flattened. Historians and restorers are highly interested in using digitalization techniques to analyze carvings in historic artifacts and monuments and to get impressions and illustrations of their original shape and appearance. Moreover, museums and churches are interested in such illustrations for presenting them to visitors. The techniques that we propose allow for detecting, selecting, and visualizing carving structures. In addition, we introduce an example-based method for generating carvings. The resulting tool, which integrates all techniques, was evaluated by three experienced restorers to assess the usefulness and applicability. Furthermore, we compared our approach with exaggerated shading and other state-of-the-art methods.

We present the first visualization tool that combines patient-specific hemodynamics with information about the vessel wall deformation and wall thickness in cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. For the patient-specific rupture risk evaluation and treatment analysis, both morphological and hemodynamic data have to be investigated. Medical researchers emphasize the importance of analyzing correlations between wall properties such as the wall deformation and thickness, and hemodynamic attributes like the Wall Shear Stress and near-wall flow. Our method uses a linked 2.5D and 3D depiction of the aneurysm together with blood flow information that enables the simultaneous exploration of wall characteristics and hemodynamic attributes during the cardiac cycle. We thus offer medical researchers an effective visual exploration tool for aneurysm treatment risk assessment. The 2.5D view serves as an overview that comprises a projection of the vessel surface to a 2D map, providing an occlusion-free surface visualization combined with a glyph-based depiction of the local wall thickness. The 3D view represents the focus upon which the data exploration takes place. To support the time-dependent parameter exploration and expert collaboration, a camera path is calculated automatically, where the user can place landmarks for further exploration of the properties. We developed a GPU-based implementation of our visualizations with a flexible interactive data exploration mechanism. We designed our techniques in collaboration with domain experts, and provide details about the evaluation.

Due to the intricate relationship between the pelvic organs and vital structures, such as vessels and nerves, pelvic anatomy is often considered to be complex to comprehend. In oncological pelvic surgery, a trade-off has to be made between complete tumor resection and preserving function by preventing damage to the nerves. Damage to the autonomic nerves causes undesirable post-operative side-effects such as fecal and urinal incontinence, as well as sexual dysfunction in up to 80 percent of the cases. Since these autonomic nerves are not visible in pre-operative MRI scans or during surgery, avoiding nerve damage during such a surgical procedure becomes challenging. In this work, we present visualization methods to represent context, target, and risk structures for surgical planning. We employ distance-based and occlusion management techniques in an atlas-based surgical planning tool for oncological pelvic surgery. Patient-specific pre-operative MRI scans are registered to an atlas model that includes nerve information. Through several interactive linked views, the spatial relationships and distances between the organs, tumor and risk zones are visualized to improve understanding, while avoiding occlusion. In this way, the surgeon can examine surgically relevant structures and plan the procedure before going into the operating theater, thus raising awareness of the autonomic nerve zone regions and potentially reducing post-operative complications. Furthermore, we present the results of a domain expert evaluation with surgical oncologists that demonstrates the advantages of our approach.

Abstract We present an Aortic Vortex Classification (AVOCLA) that allows to classify vortices in the human aorta semi‐automatically. Current medical studies assume a strong relation between cardiovascular diseases and blood flow patterns such as vortices. Such vortices are extracted and manually classified according to specific, unstandardized properties. We employ an agglomerative hierarchical clustering to group vortex‐representing path lines as basis for the subsequent classification. Classes are based on the vortex' size, orientation and shape, its temporal occurrence relative to the cardiac cycle as well as its spatial position relative to the vessel course. The classification results are presented by a 2D and 3D visualization technique. To confirm the usefulness of both approaches, we report on the results of a user study. Moreover, AVOCLA was applied to 15 datasets of healthy volunteers and patients with different cardiovascular diseases. The results of the semi‐automatic classification were qualitatively compared to a manually generated ground truth of two domain experts considering the vortex number and five specific properties.

Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.

We present the first visualization tool that combines pathlines from blood flow and wall thickness information. Our method uses illustrative techniques to provide occlusion-free visualization of the flow. We thus offer medical researchers an effective visual analysis tool for aneurysm treatment risk assessment. Such aneurysms bear a high risk of rupture and significant treatment-related risks. Therefore, to get a fully informed decision it is essential to both investigate the vessel morphology and the hemodynamic data. Ongoing research emphasizes the importance of analyzing the wall thickness in risk assessment. Our combination of blood flow visualization and wall thickness representation is a significant improvement for the exploration and analysis of aneurysms. As all presented information is spatially intertwined, occlusion problems occur. We solve these occlusion problems by dynamic cutaway surfaces. We combine this approach with a glyph-based blood flow representation and a visual mapping of wall thickness onto the vessel surface. We developed a GPU-based implementation of our visualizations which facilitates wall thickness analysis through real-time rendering and flexible interactive data exploration mechanisms. We designed our techniques in collaboration with domain experts, and we provide details about the evaluation of the technique and tool.

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.

Abstract The investigation of hemodynamic information for the assessment of cardiovascular diseases (CVDs) gained importance in recent years. Improved flow measuring modalities and computational fluid dynamics (CFD) simulations yield in reliable blood flow information. For a visual exploration of the flow information, domain experts are used to investigate the flow information combined with its enclosed vessel anatomy. Since the flow is spatially embedded in the surrounding vessel surface, occlusion problems have to be resolved. A visual reduction of the vessel surface that still provides important anatomical features is required. We accomplish this by applying an adaptive surface visualization inspired by the suggestive contour measure. Furthermore, an illustration is employed to highlight the animated pathlines and to emphasize nearby surface regions. Our approach combines several visualization techniques to improve the perception of surface shape and depth. Thereby, we ensure appropriate visibility of the embedded flow information, which can be depicted with established or advanced flow visualization techniques. We apply our approach to cerebral aneurysms and aortas with simulated and measured blood flow. An informal user feedback with nine domain experts, we confirm the advantages of our approach compared with existing methods, e.g. semi‐transparent surface rendering. Additionally, we assessed the applicability and usefulness of the pathline animation with highlighting nearby surface regions.

Abstract We present a novel line drawing algorithm that illustrates surfaces in real‐time to convey their shape. We use line integral convolution (LIC) and employ ambient occlusion for illustrative surface rendering. Furthermore, our method depicts salient regions based on the illumination gradient. Our method works on animated surfaces in a frame‐coherent manner. Therefore, it yields an illustrative representation of time‐dependent surfaces as no preprocessing step is needed. In this paper, the method is used to highlight the structure of molecular surfaces and to illustrate important surface features like cavities, channels, and pockets. The benefit of our method was evaluated with domain experts. We also demonstrate the applicability of our method to medical visualization.

Abstract A pathological vessel dilation in the brain, termed cerebral aneurysm, bears a high risk of rupture, and is associated with a high mortality. In recent years, incidental findings of unruptured aneurysms have become more frequent, mainly due to advances in medical imaging. The pathological condition is often treated with a stent that diverts the blood flow from the aneurysm sac back to the original vessel. Prior to treatment, neuroradiologists need to decide on the optimal stent configuration and judge the long‐term rupture risk, for which blood flow information is essential. Modern patient‐specific simulations can model the hemodynamics for various stent configurations, providing important indicators to support the decision‐making process. However, the necessary visual analysis of these data becomes tedious and time‐consuming, because of the abundance of information. We introduce a comprehensive comparative visualization that integrates morphology with blood flow indicators to facilitate treatment assessment. To deal with the visual complexity, we propose a details‐on‐demand approach, combining established medical visualization techniques with innovative glyphs inspired by information visualization concepts. In an evaluation we have obtained informal feedback from domain experts, gauging the value of our visualization.

For an individual rupture risk assessment of aneurysms, the aneurysm's wall morphology and hemodynamics provide valuable information. Hemodynamic information is usually extracted via computational fluid dynamic (CFD) simulation on a previously extracted 3D aneurysm surface mesh or directly measured with 4D phase-contrast magnetic resonance imaging. In contrast, a noninvasive imaging technique that depicts the aneurysm wall in vivo is still not available. Our approach comprises an experiment, where intravascular ultrasound (IVUS) is employed to probe a dissected saccular aneurysm phantom, which we modeled from a porcine kidney artery. Then, we extracted a 3D surface mesh to gain the vessel wall thickness and hemodynamic information from a CFD simulation. Building on this, we developed a framework that depicts the inner and outer aneurysm wall with dedicated information about local thickness via distance ribbons. For both walls, a shading is adapted such that the inner wall as well as its distance to the outer wall is always perceivable. The exploration of the wall is further improved by combining it with hemodynamic information from the CFD simulation. Hence, the visual analysis comprises a brushing and linking concept for individual highlighting of pathologic areas. Also, a surface clustering is integrated to provide an automatic division of different aneurysm parts combined with a risk score depending on wall thickness and hemodynamic information. In general, our approach can be employed for vessel visualization purposes where an inner and outer wall has to be adequately represented.

Epidemiological population studies impose information about a set of subjects (a cohort) to characterize disease-specific risk factors. Cohort studies comprise heterogenous variables describing the medical condition as well as demographic and lifestyle factors and, more recently, medical image data. We propose an Interactive Visual Analysis (IVA) approach that enables epidemiologists to rapidly investigate the entire data pool for hypothesis validation and generation. We incorporate image data, which involves shape-based object detection and the derivation of attributes describing the object shape. The concurrent investigation of image-based and non-image data is realized in a web-based multiple coordinated view system, comprising standard views from information visualization and epidemiological data representations such as pivot tables. The views are equipped with brushing facilities and augmented by 3D shape renderings of the segmented objects, e.g., each bar in a histogram is overlaid with a mean shape of the associated subgroup of the cohort. We integrate an overview visualization, clustering of variables and object shape for data-driven subgroup definition and statistical key figures for measuring the association between variables. We demonstrate the IVA approach by validating and generating hypotheses related to lower back pain as part of a qualitative evaluation.

Abstract Line drawing techniques are important methods to illustrate shapes. Existing feature line methods, e.g., suggestive contours, apparent ridges, or photic extremum lines, solely determine salient regions and illustrate them with separate lines. Hatching methods convey the shape by drawing a wealth of lines on the whole surface. Both approaches are often not sufficient for a faithful visualization of organic surface models, e.g., in biology or medicine. In this paper, we present a novel object‐space line drawing algorithm that conveys the shape of such surface models in real‐time. Our approach employs contour‐ and feature‐based illustrative streamlines to convey surface shape (ConFIS). For every triangle, precise streamlines are calculated on the surface with a given curvature vector field. Salient regions are detected by determining maxima and minima of a scalar field. Compared with existing feature lines and hatching methods, ConFIS uses the advantages of both categories in an effective and flexible manner. We demonstrate this with different anatomical and artificial surface models. In addition, we conducted a qualitative evaluation of our technique to compare our results with exemplary feature line and hatching methods.

Abstract Surface models derived from medical image data often exhibit artefacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re‐evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation of uniform and anisotropic filters, model quality is evaluated in real‐time and provided to the user to support the mental optimization of input parameters. This is achieved by means of quality graphs and quality bars. Moreover, this framework is used to find appropriate smoothing parameters automatically and to provide data‐specific parameter suggestions. These suggestions are employed to generate a preview gallery with different smoothing suggestions. The preview functionality is additionally used for the inspection of specific artefacts and their possible reduction with different parameter sets.

Abstract The qualitative exploration of near‐wall hemodynamics in cerebral aneurysms provides important insights for risk assessment. For instance, a direct relation between complex flow patterns and aneurysm formation could be observed. Due to the high complexity of the underlying time‐dependent flow data, the exploration is challenging, in particular for medical researchers not familiar with such data. We present the AmniVis‐Explorer, a system that is designed for the preparation of a qualitative medical study. The provided features were developed in close collaboration with medical researchers involved in the study. This comprises methods for a purposeful selection of surface regions of interest and a novel approach to provide a 2D overview of flow patterns that are represented by streamlines at these regions. Furthermore, we present a specialized interface that supports binary classification of patterns and temporal exploration as well as methods for selection, highlighting and automatic 3D navigation to particular patterns. Based on eight representative datasets, we conducted informal interviews with two bord‐certified radiologists and a flow expert to evaluate the system. It was confirmed that the AmniVis‐Explorer allows for an easy selection, qualitative exploration and classification of near‐wall flow patterns that are represented by streamlines.